Awesome-MCP-ZH

# Awesome-MCP-ZH  Welcome to `Awesome-MCP-ZH`, a resource collection specifically designed for Chinese users of MCP (Model Context Protocol)! Here you will find a basic introduction to MCP, gameplay, clients, servers, and community resources to help you quickly get started with this "universal plug" in the AI world. [](README.md) [](README.md) - Author: 云中江树 (WeChat Official Account: 云中江树) - If friends in China want to quickly experience MCP capabilities for free, I recommend the combination of Cherry Studio (client) + 阿里 Qwen (large model), which is advantageous because it is free, easy to operate, and does not require magic or recharge for LLM. - My experience with LLM selection is: Claude3.7 > Qwen2.5-Max > DeepSeek --- ## What is MCP? MCP stands for **模型上下文协议 (Model Context Protocol)**, launched by Anthropic in November 2024, and is an open-source communication standard. Simply put, it equips AI with a "super network cable," allowing AI to seamlessly connect with external tools, data, and systems. - **Metaphor**: AI is a smart but homebound nerd, and MCP is its "delivery person," helping it fetch data and get work done. - **Goal**: To enable AI to do more than just chat, but to take real actions, such as querying databases, sending emails, and writing code.  Want to learn more? Check out the [official introduction](https://www.anthropic.com/news/model-context-protocol). --- ## What Can MCP Do? MCP can transform AI from a "talking head" into a "doer." Here are a few examples: 1. **Connect Tools**: Send messages via Slack, manage code on GitHub, create 3D models with Blender. 2. **Query Data**: Directly access your computer files, database records, and even real-time information online. 3. **Handle Complex Tasks**: While writing a webpage, AI can check code, generate images, and debug pages, all in one go. 4. **Human-Machine Collaboration**: AI does half the work and asks for your opinion; it continues only after you nod. **Example**: Install a Slack MCP server in Cursor, and AI can write code while sending messages to notify the team, making it super convenient! --- ## MCP Clients The MCP client is the "control panel" for AI. Here are some popular choices: - **Claude Desktop** - **Introduction**: Claude desktop version, usable by ordinary people. - **Features**: Official client that connects to various MCP servers, such as connecting to Blender MCP to create 3D models using natural language. - **Link**: [Anthropic Official Website](https://docs.anthropic.com) - **Screenshot**:  - **Tips**: No coding required, beginner-friendly. - **Cherry Studio** - **Introduction**: Emerging client that supports visual configuration. - **Features**: Easily configure MCP servers with point-and-click. - **Link**: [Cherry Studio](https://github.com/CherryHQ/cherry-studio) - **Screenshot**:  - **Tips**: Under development, keep an eye on community updates. - **5ire** - **Introduction**: A modern AI assistant and MCP client that supports various mainstream service providers. - **Features**: Connects tools and data sources via the MCP protocol, providing file system access, database interaction, remote data retrieval, etc.; supports local knowledge bases, usage analytics, prompt libraries, bookmarks, and quick search features. - **Link**: [5ire Official Website](https://5ire.app/) | [GitHub Repository](https://github.com/nanbingxyz/5ire) - **Screenshot**:  - **Tips**: Suitable for both developers and non-developers, supports multiple platforms (Windows, macOS, Linux). - **Cursor** - **Introduction**: A code editor that becomes an "all-rounder" with MCP. - **Features**: Write code, send Slack messages, generate images. - **Link**: [Official Website](https://cursor.sh/) - **Screenshot**:  - **Tips**: A must-have for programmers, try connecting to GitHub MCP. - **DeepChat** - **Introduction**: An intelligent assistant that connects powerful AI with personal worlds. - **Features**: Supports multiple model cloud services (like DeepSeek, OpenAI, etc.) and local model deployment (like Ollama), with multi-channel chat concurrency support, complete Markdown rendering, local file handling, and MCP support. - **Link**: [DeepChat Official Website](https://deepchat.thinkinai.xyz/) | [GitHub Repository](https://github.com/ThinkInAIXYZ/deepchat) - **Screenshot**:  - **Tips**: Suitable for both developers and non-developers, supports multiple platforms (Windows, macOS, Linux), can be quickly integrated into existing workflows via MCP. - **ChatWise** - **Introduction**: A powerful tool that emphasizes privacy protection. - **Features**: Supports any LLM model (like GPT-4, Claude, Gemini, etc.), multi-modal chat (audio, PDF, images, text, etc.), web search (Tavily API or local browser), MCP tool integration (like Notion, Google Sheets, etc.), and real-time rendering of HTML/React/charts. - **Link**: [ChatWise Official Website](https://chatwise.app/) | [Documentation](https://docs.chatwise.app/) - **Screenshot**:  - **Tips**: Data is stored entirely locally, suitable for users needing efficient tools; extend its functionality through MCP! --- ## Selected List of MCP Servers MCP servers are the "toolboxes" that empower AI models to interact with external tools, data, and systems. Below is a curated list of MCP servers sorted by different application scenarios, organized by scenario and quality (official/reference > commonly used/mature > community/specific), making it easier for Chinese users to find and use. **Note:** * **Name:** Click to jump to the corresponding GitHub repository. * **Chinese Introduction:** Briefly describes the main functions and uses of the server. * **Remarks:** Includes developer information (such as official, community), main technologies, applicable platforms, or key features. --- ### 🌐 Browser Automation and Web Interaction *(Enables AI to browse the web like a human, extract information, fill out forms, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [microsoft/playwright-mcp](https://github.com/microsoft/playwright-mcp) | Officially produced by Microsoft, uses Playwright to allow AI to precisely control web pages and automate data scraping. | Official implementation, highly recommended for browser automation, suitable for scenarios requiring fine web interaction. | | [browserbase/mcp-server-browserbase](https://github.com/browserbase/mcp-server-browserbase) | Cloud-based browser automation service that can navigate web pages, extract data, fill out forms, etc., without local installation. | Official implementation, developed in TypeScript, a versatile cloud browser operation tool. | | [modelcontextprotocol/server-puppeteer](https://github.com/modelcontextprotocol/servers/tree/main/src/puppeteer) | Official reference implementation, uses Puppeteer for browser automation and web scraping. | Official reference, developed in TypeScript, basic tool for web scraping and interaction. | | [apify/actors-mcp-server](https://github.com/apify/actors-mcp-server) | Integrates over 3000 cloud tools from the Apify platform for data extraction from websites, e-commerce, social media, etc. | Official implementation, developed in TypeScript, cloud data scraping tool library. | | [AgentQL](https://github.com/tinyfish-io/agentql-mcp) | Allows AI agents to obtain structured data from unstructured web pages. | Official implementation, developed in Python, web data structuring extraction. | | [Firecrawl](https://github.com/mendableai/firecrawl-mcp-server) | Uses Firecrawl to extract web data, supporting JavaScript rendering. | Official implementation, developed in TypeScript, advanced web scraping. | | [Oxylabs](https://github.com/oxylabs/oxylabs-mcp) | Uses Oxylabs Web API to scrape websites, supporting dynamic rendering and structured data extraction. | Official implementation, developed in Python, professional-level web scraping. | | [Hyperbrowser](https://github.com/hyperbrowserai/mcp) | Next-generation AI agent browser automation platform, supporting large-scale, seamless operations. | Official implementation, developed in TypeScript, large-scale browser automation. | | [ScreenshotOne](https://github.com/screenshotone/mcp/) | Uses ScreenshotOne service to render website screenshots. | Official implementation, developed in TypeScript, web screenshot tool. | | [modelcontextprotocol/server-fetch](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch) | Official reference implementation, flexibly retrieves web content (HTML/JSON/MD) and optimizes it for AI processing. | Official reference, developed in Python, basic web content retrieval. | | [RAG Web Browser](https://github.com/apify/mcp-server-rag-web-browser) | Apify open-source tool that performs web searches, scrapes URLs, and returns content in Markdown format. | Community implementation (Apify), developed in TypeScript, web browsing combined with RAG. | | [scrapling-fetch](https://github.com/cyberchitta/scrapling-fetch-mcp) | Retrieves text content from websites with anti-scraping measures. | Community implementation, developed in Python, bypasses anti-scraping. | | [jae-jae/fetcher-mcp](https://github.com/jae-jae/fetcher-mcp) | Uses Playwright headless browser to retrieve web content, supporting JS rendering and intelligent extraction. | Community implementation, developed in TypeScript, Playwright web content extraction. | --- ### 💻 Development and Code Execution *(Enables AI to run code, analyze codebases, integrate with development tools, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [yzfly/mcp-python-interpreter](https://github.com/yzfly/mcp-python-interpreter) | A secure, standardized Python environment that supports code execution, environment, and package management. | Community benchmark, lightweight Python execution environment, suitable for development and data analysis. | | [pydantic/pydantic-ai/mcp-run-python](https://github.com/pydantic/pydantic-ai) | Produced by Pydantic, runs Python code in a secure sandbox environment, suitable for developing programming agents. | Official implementation (Pydantic), developed in Python, secure code execution. | | [E2B](https://github.com/e2b-dev/mcp-server) | Runs code in a secure cloud sandbox provided by E2B. | Official implementation, developed in TypeScript, cloud-based secure code sandbox. | | [JetBrains](https://github.com/JetBrains/mcp-jetbrains) | Official integration by JetBrains, allows AI to handle code in JetBrains IDE. | Official implementation, developed in Kotlin, IDE code operations. | | [admica/FileScopeMCP](https://github.com/admica/FileScopeMCP) | Analyzes codebase dependencies, generates graphs to help AI understand project structure. | Community implementation, multi-language (Py/TS/Rust), code structure analysis. | | [mem0ai/mem0-mcp](https://github.com/mem0ai/mem0-mcp) | Manages code preferences and patterns, supports semantic search, facilitating access to technical documentation in IDEs. | Official implementation, developed in Python, programmer's memory assistant and preference management. | | [code-executor](https://github.com/bazinga012/mcp_code_executor) | Allows AI to execute Python code in a specified Conda environment. | Community implementation, developed in Python, Conda environment code execution. | | [code-sandbox-mcp](https://github.com/Automata-Labs-team/code-sandbox-mcp) | Creates a secure Docker container environment to execute code. | Community implementation, developed in Python, Docker sandbox code execution. | | [ForeverVM](https://github.com/jamsocket/forevervm/tree/main/javascript/mcp-server) | Runs Python code in a code sandbox. | Official implementation (Jamsocket), developed in JavaScript, code sandbox. | | [Riza](https://github.com/riza-io/riza-mcp) | A platform provided by Riza for executing any code and using tools. | Official implementation, developed in Go, general code execution platform. | | [Semgrep](https://github.com/semgrep/mcp) | Allows AI agents to use Semgrep for code security scanning. | Official implementation, developed in Python, code security scanning. | | [ZenML](https://github.com/zenml-io/mcp-zenml) | Interacts with the ZenML MLOps/LLMOps platform to manage machine learning workflows. | Official implementation, developed in Python, MLOps workflow management. | | [vivekVells/mcp-pandoc](https://github.com/vivekVells/mcp-pandoc) | Uses Pandoc for seamless document format conversion (Markdown, HTML, PDF, DOCX, etc.). | Community implementation, developed in Python, document format conversion. | | [iTerm MCP](https://github.com/ferrislucas/iterm-mcp) | Integrates macOS's iTerm2 terminal, allowing AI to execute and monitor terminal commands. | Community implementation, developed in Python, macOS terminal control. | | [Windows CLI](https://github.com/SimonB97/win-cli-mcp-server) | Safely executes command line commands (PowerShell, CMD, Git Bash) on Windows systems. | Community implementation, developed in Python, Windows command line control. | --- ### 🔄 Version Control (Git / GitHub / GitLab) *(Enables AI to operate code repositories, manage Pull Requests, handle Issues, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [modelcontextprotocol/server-github](https://github.com/modelcontextprotocol/servers) | Official reference implementation, integrates GitHub API to manage repositories, files, PRs, and Issues. | Official reference, developed in TypeScript, essential for heavy GitHub users. | | [modelcontextprotocol/server-git](https://github.com/modelcontextprotocol/servers/tree/main/src/git) | Official reference implementation, directly operates local Git repositories for reading, searching, and analysis. | Official reference, developed in Python, local Git repository operations. | | [modelcontextprotocol/server-gitlab](https://github.com/modelcontextprotocol/servers/tree/main/src/gitlab) | Official reference implementation, integrates GitLab API for project management and CI/CD operations. | Official reference, developed in TypeScript, suitable for GitLab users. | | [Gitee](https://github.com/oschina/mcp-gitee) | Official integration for Gitee, managing Gitee repositories, Issues, and Pull Requests. | Official implementation, developed in Go, essential for Gitee users. | | [Github Actions](https://github.com/ko1ynnky/github-actions-mcp-server) | Interacts with Github Actions to manage workflows. | Community implementation, developed in TypeScript, GitHub Actions management. | --- ### 🗄️ Database Interaction *(Enables AI to query databases, check table structures, and even modify data.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [ClickHouse/mcp-clickhouse](https://github.com/ClickHouse/mcp-clickhouse) | Official integration for ClickHouse, connects to ClickHouse database for querying and schema checking. | Official implementation, developed in Python, ClickHouse data analysis tool. | | [modelcontextprotocol/server-postgres](https://github.com/modelcontextprotocol/servers) | Official reference implementation, integrates PostgreSQL, supports querying and schema analysis. | Official reference, developed in TypeScript, PostgreSQL database operations. | | [Chroma](https://github.com/chroma-core/chroma-mcp) | Official integration for Chroma, used for embedding, vector search, document storage, and full-text search. | Official implementation, developed in Python, AI application database, vector search. | | [Aiven](https://github.com/Aiven-Open/mcp-aiven) | Official integration for Aiven, navigates Aiven projects and interacts with PostgreSQL®, Kafka®, ClickHouse®, OpenSearch® services. | Official implementation, developed in Python, Aiven cloud database management. | | [Milvus](https://github.com/zilliztech/mcp-server-milvus) | Official integration for Zilliz/Milvus, searches, queries, and interacts with data in the Milvus vector database. | Official implementation, developed in Python, Milvus vector database operations. | | [MotherDuck](https://github.com/motherduckdb/mcp-server-motherduck) | Official integration for MotherDuck, queries and analyzes data using MotherDuck and local DuckDB. | Official implementation, developed in Python, DuckDB cloud service interaction. | | [Neo4j](https://github.com/neo4j-contrib/mcp-neo4j/) | Official contribution for Neo4j, operates Neo4j graph database (schema + read/write Cypher), and provides memory functions supported by graph databases. | Official contribution, developed in Python, graph database operations and memory. | | [Neon](https://github.com/neondatabase/mcp-server-neon) | Official integration for Neon, interacts with the Neon serverless Postgres platform. | Official implementation, developed in TypeScript, Neon Serverless PG management. | | [Qdrant](https://github.com/qdrant/mcp-server-qdrant/) | Official integration for Qdrant, implements a semantic memory layer based on the Qdrant vector search engine. | Official implementation, developed in Python, Qdrant vector search and memory. | | [SingleStore](https://github.com/singlestore-labs/mcp-server-singlestore) | Official integration for SingleStore, interacts with the SingleStore database platform. | Official implementation, developed in Python, SingleStore database operations. | | [StarRocks](https://github.com/StarRocks/mcp-server-starrocks) | Official integration for StarRocks, interacts with the StarRocks database. | Official implementation, developed in Python, StarRocks data warehouse interaction. | | [Tinybird](https://github.com/tinybirdco/mcp-tinybird) | Official integration for Tinybird, interacts with the Tinybird serverless ClickHouse platform. | Official implementation, developed in Python, Tinybird platform interaction. | | [modelcontextprotocol/server-redis](https://github.com/modelcontextprotocol/servers/tree/main/src/redis) | Official reference implementation, interacts with Redis key-value storage. | Official reference, developed in TypeScript, Redis cache/storage operations. | | [modelcontextprotocol/server-sqlite](https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite) | Official reference implementation, operates SQLite databases and includes business intelligence capabilities. | Official reference, developed in Python, local SQLite database operations. | | [BigQuery (by LucasHild)](https://github.com/LucasHild/mcp-server-bigquery) | Allows AI to check BigQuery database schemas and execute queries. | Community implementation, developed in Python, Google BigQuery queries. | | [MySQL (by designcomputer)](https://github.com/designcomputer/mysql_mcp_server) | Python implementation of MySQL integration, with access control and schema checking. | Community implementation, developed in Python, MySQL database operations. | | [MongoDB Lens](https://github.com/furey/mongodb-lens) | A fully functional MongoDB database MCP server. | Community implementation, developed in TypeScript, advanced MongoDB operations. | | [DBHub](https://github.com/bytebase/dbhub/) | A general database MCP server that can connect to MySQL, PostgreSQL, SQLite, DuckDB, etc. | Community implementation (Bytebase), developed in TypeScript, supports multiple databases. | --- ### ☁️ Cloud Platform and Service Integration (AWS, Cloudflare, etc.) *(Enables AI to manage cloud resources, call cloud service APIs, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [Cloudflare](https://github.com/cloudflare/mcp-server-cloudflare) | Official integration for Cloudflare, deploys, configures, and queries Cloudflare developer platform resources (Workers/KV/R2/D1). | Official implementation, developed in TypeScript, Cloudflare platform management. | | [alexei-led/aws-mcp-server](https://github.com/alexei-led/aws-mcp-server) | A lightweight server that allows AI to execute AWS CLI commands, supporting secure Docker operation. | Community implementation, developed in Python, manages AWS via CLI. | | [AWS KB Retrieval](https://github.com/modelcontextprotocol/servers/tree/main/src/aws-kb-retrieval-server) | Official reference implementation that retrieves information from the AWS knowledge base using Bedrock Agent Runtime. | Official reference, developed in TypeScript, AWS Bedrock knowledge base. | | [AWS Resources Operations](https://github.com/baryhuang/mcp-server-aws-resources-python) | Runs generated Python code to safely query or modify any AWS resource supported by boto3. | Community implementation, developed in Python, manages AWS resources via Boto3. | | [AWS S3](https://github.com/aws-samples/sample-mcp-server-s3) | AWS official example, flexibly retrieves objects from S3 (such as PDF documents). | Official example (AWS), developed in TypeScript, S3 file retrieval. | | [Pulumi](https://github.com/dogukanakkaya/pulumi-mcp-server) | Interacts with the Pulumi API to create and list Stacks (Infrastructure as Code). | Community implementation, developed in Go, Pulumi IaC management. | | [Kubernetes (Go)](https://github.com/strowk/mcp-k8s-go) | A Kubernetes server implemented in Go, used to browse Pods, logs, events, namespaces, etc. | Community implementation, developed in Go, Kubernetes cluster management. | | [Kubernetes and OpenShift](https://github.com/manusa/kubernetes-mcp-server) | A powerful Kubernetes MCP server that additionally supports OpenShift, providing CRUD operations and dedicated tools. | Community implementation, developed in Go, advanced management for Kubernetes/OpenShift. | | [VolcEngine TOS](https://github.com/dinghuazhou/sample-mcp-server-tos) | Official example from VolcEngine, flexibly retrieves objects from VolcEngine Object Storage (TOS). | Official example (VolcEngine), developed in TypeScript, VolcEngine TOS file retrieval. | --- ### 🔍 Search *(Enables AI to call various search engines or specialized search services.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [Exa](https://github.com/exa-labs/exa-mcp-server) | Official integration for Exa, using a search engine designed specifically for AI. | Official implementation, developed in TypeScript, AI-specific search engine. | | [Brave Search](https://github.com/modelcontextprotocol/servers/tree/main/src/brave-search) | Official reference implementation, uses Brave's search API for web and local searches. | Official reference, developed in TypeScript, Brave search engine. | | [Tavily](https://github.com/tavily-ai/tavily-mcp) | Official integration for Tavily, a search engine designed for AI agents (search + extraction). | Official implementation, developed in Python, AI agent-specific search engine. | | [Perplexity](https://github.com/ppl-ai/modelcontextprotocol) | Official integration for Perplexity, connects to the Perplexity Sonar API for real-time web research. | Official implementation, developed in Python, Perplexity real-time search. | | [Kagi Search](https://github.com/kagisearch/kagimcp) | Official integration for Kagi, uses Kagi's search API for web searches. | Official implementation, developed in Python, Kagi search engine. | | [Search1API](https://github.com/fatwang2/search1api-mcp) | Official integration for Search1API, an API that implements search, scraping, and sitemap functionalities. | Official implementation, developed in TypeScript, multi-functional search API. | | [Google Custom Search](https://github.com/adenot/mcp-google-search) | Provides Google search results via the Google Custom Search API. | Community implementation, developed in TypeScript, Google Custom Search. | | [Bing Web Search API](https://github.com/leehanchung/bing-search-mcp) | Server implementation of Microsoft's Bing Web Search API. | Community implementation, developed in Python, Bing search. | | [SearXNG](https://github.com/ihor-sokoliuk/mcp-searxng) | Connects to SearXNG meta-search engine instances. | Community implementation, developed in TypeScript, SearXNG meta-search. | | [mcp-local-rag](https://github.com/nkapila6/mcp-local-rag) | Locally running RAG-style web search, using MediaPipe Embedder and DuckDuckGo. | Community implementation, developed in Python, local RAG search (no API Key required). | --- ### 💬 Communication and Collaboration (Slack, Email, etc.) *(Enables AI to send and receive messages, manage schedules, participate in team collaboration, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [modelcontextprotocol/server-slack](https://github.com/modelcontextprotocol/servers) | Official reference implementation, integrates Slack, allowing AI to manage channels and send messages. | Official reference, developed in TypeScript, Slack team collaboration. | | [Inbox Zero](https://github.com/elie222/inbox-zero/tree/main/apps/mcp-server) | Official integration for Inbox Zero, AI personal email assistant. | Official implementation, developed in Python, intelligent email management. | | [gotoHuman](https://github.com/gotohuman/gotohuman-mcp-server) | Official integration for gotoHuman, allows AI agents to send requests to humans for approval. | Official implementation, developed in TypeScript, human-machine collaboration approval. | | [ClaudePost](https://github.com/ZilongXue/claude-post) | Implements seamless email management for Gmail, supporting email search, reading, and sending. | Community implementation, developed in Python, Gmail email operations. | | [Gmail](https://github.com/GongRzhe/Gmail-MCP-Server) | Supports automated authentication for Gmail integration, used with Claude Desktop. | Community implementation, developed in Python, Gmail integration (with authentication). | | [Gmail Headless](https://github.com/baryhuang/mcp-headless-gmail) | A remotely hosted Gmail server that can send and receive emails without local credentials or file systems. | Community implementation, developed in Python, remote Gmail operations. | | [Google Calendar (by v-3)](https://github.com/v-3/google-calendar) | Integrates Google Calendar, checks schedules, finds free time, adds/deletes events. | Community implementation, developed in TypeScript, Google Calendar management. | | [Apple Calendar](https://github.com/Omar-v2/mcp-ical) | Interacts with macOS Calendar, creates/modifies events, lists schedules, finds free time slots, etc. | Community implementation, developed in Python, macOS calendar management. | | [Discord (by v-3)](https://github.com/v-3/discordmcp) | Connects to Discord servers via bots, reads and writes channel messages. | Community implementation, developed in TypeScript, Discord message interaction. | | [Telegram](https://github.com/chigwell/telegram-mcp) | Integrates Telegram via Telethon, supports paginated reading of chats, retrieval, and sending messages. | Community implementation, developed in Python, Telegram message interaction. | | [LINE](https://github.com/amornpan/py-mcp-line) | Integrates LINE Bot, allowing AI to read and analyze LINE conversations. | Community implementation, developed in Python, LINE conversation analysis. | | [X (Twitter) (by vidhupv)](https://github.com/vidhupv/x-mcp) | Directly creates, manages, and publishes X/Twitter tweets via Claude. | Community implementation, developed in Python, Twitter tweet management. | --- ### 💰 Finance and Cryptocurrency *(Enables AI to obtain financial data, analyze stocks, interact with blockchains, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [Stripe](https://github.com/stripe/agent-toolkit) | Official integration for Stripe, interacts with Stripe API to handle payments, customers, and refunds. | Official implementation, developed in TypeScript, Stripe payment processing. | | [Chargebee](https://github.com/chargebee/agentkit/tree/main/modelcontextprotocol) | Official integration for Chargebee, connecting AI agents to the Chargebee billing platform. | Official implementation, developed in TypeScript, Chargebee billing management. | | [Financial Datasets](https://github.com/financial-datasets/mcp-server) | Stock market API designed for AI agents. | Official implementation, developed in Python, AI-friendly stock data. | | [narumiruna/yfinance-mcp](https://github.com/narumiruna/yfinance-mcp) | Uses Yahoo Finance API to obtain financial data, facilitating stock analysis. | Community implementation, developed in Python, Yahoo Finance data retrieval. | | [AlphaVantage](https://github.com/calvernaz/alphavantage) | Server for AlphaVantage stock market data API. | Community implementation, developed in Python, AlphaVantage financial data. | | [Thirdweb](https://github.com/thirdweb-dev/ai/tree/main/python/thirdweb-mcp) | Official integration for Thirdweb, reads and writes to 2000+ blockchains, queries data, analyzes/deploys contracts, executes transactions. | Official implementation, developed in Python, multi-chain blockchain interaction. | | [BICScan](https://github.com/ahnlabio/bicscan-mcp) | Retrieves risk scores/assets held for EVM blockchain addresses (EOA, CA, ENS) or even domains. | Official implementation, developed in Python, blockchain address risk analysis. | | [Bankless Onchain](https://github.com/bankless/onchain-mcp) | Queries on-chain data, such as ERC20 tokens, transaction history, smart contract status. | Official implementation, developed in TypeScript, on-chain data querying. | | [EVM MCP Server](https://github.com/mcpdotdirect/evm-mcp-server) | Provides comprehensive blockchain services for 30+ EVM networks, supporting tokens, NFTs, smart contracts, transactions, and ENS. | Community implementation, developed in TypeScript, EVM multi-chain services. | | [Bsc-mcp](https://github.com/TermiX-official/bsc-mcp) | Connects AI with BNB Chain, executing complex on-chain operations (transfers, trades, security checks, etc.). | Community implementation, developed in Python, BNB Chain operations. | | [Solana Agent Kit](https://github.com/sendaifun/solana-agent-kit/tree/main/examples/agent-kit-mcp-server) | Interacts with the Solana blockchain using the Solana Agent Kit, supporting 40+ protocol operations. | Community implementation, developed in TypeScript, Solana chain interaction. | --- ### 📁 File System and Storage *(Enables AI to access local files, operate cloud storage, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [modelcontextprotocol/server-filesystem](https://github.com/modelcontextprotocol/servers) | Official reference implementation, providing direct access to the local file system with configurable permissions. | Official reference, developed in TypeScript, local file system operations. | | [Google Drive](https://github.com/modelcontextprotocol/servers/tree/main/src/gdrive) | Official reference implementation, integrates Google Drive for listing, reading, and searching files. | Official reference, developed in TypeScript, Google Drive file management. | | [Box](https://github.com/box-community/mcp-server-box) | Official integration for Box, interacts with Box AI and intelligent content management platform. | Official implementation, developed in Python, Box cloud storage interaction. | | [Fireproof](https://github.com/fireproof-storage/mcp-database-server) | Official integration for Fireproof, an immutable ledger database that supports real-time synchronization. | Official implementation, developed in TypeScript, distributed database synchronization. | | [Golang Filesystem Server](https://github.com/mark3labs/mcp-filesystem-server) | Secure file operations implemented in Go, with configurable access control. | Community implementation, developed in Go, local file system operations (Go). | | [Everything Search](https://github.com/mamertofabian/mcp-everything-search) | Quickly searches files on Windows/macOS/Linux (using Everything/mdfind/locate). | Community implementation, developed in Python, cross-platform fast file search. | | [Obsidian Markdown Notes](https://github.com/calclavia/mcp-obsidian) | Reads and searches Obsidian libraries or any directory containing Markdown notes. | Community implementation, developed in TypeScript, Obsidian/Markdown file access. | --- ### 📊 Data Analysis, Processing, and Visualization *(Enables AI to process tabular data, generate charts, conduct data exploration, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [yzfly/mcp-excel-server](https://github.com/yzfly/mcp-excel-server) | MCP server that interacts with Excel using natural language. | Community benchmark, Excel read/write, analysis, visualization. | | [GreptimeDB](https://github.com/GreptimeTeam/greptimedb-mcp-server) | Official integration for GreptimeDB, allowing AI to safely explore and analyze time-series data in GreptimeDB. | Official implementation, developed in Python, GreptimeDB time-series data analysis. | | [Axiom](https://github.com/axiomhq/mcp-server-axiom) | Official integration for Axiom, querying and analyzing Axiom logs, traces, and event data using natural language. | Official implementation, developed in Python, Axiom log analysis. | | [Comet Opik](https://github.com/comet-ml/opik-mcp) | Official integration for Comet, querying and analyzing Opik logs, traces, prompts, and LLM telemetry using natural language. | Official implementation, developed in TypeScript, LLM observability data analysis. | | [Keboola](https://github.com/keboola/keboola-mcp-server) | Official integration for Keboola, building data workflows, integration, and analysis on a single platform. | Official implementation, developed in Python, Keboola data platform. | | [Excel (by haris-musa)](https://github.com/haris-musa/excel-mcp-server) | Excel operations, including reading/writing, worksheet management, formatting, charts, and pivot tables. | Community implementation, developed in Python, advanced Excel operations. | | [Data Exploration](https://github.com/reading-plus-ai/mcp-server-data-exploration) | Conducts autonomous data exploration on .csv datasets, easily gaining intelligent insights (**Note: will execute code**). | Community implementation, developed in Python, automatic exploration of CSV data. | | [Dataset Viewer](https://github.com/privetin/dataset-viewer) | Browses and analyzes Hugging Face datasets, supporting search, filtering, statistics, and export. | Community implementation, developed in Python, HuggingFace dataset browsing. | | [Vega-Lite](https://github.com/isaacwasserman/mcp-vegalite-server) | Generates visual charts from retrieved data using Vega-Lite format and renderer. | Community implementation, developed in Python, data visualization generation. | | [QuickChart](https://github.com/GongRzhe/Quickchart-MCP-Server) | Generates charts using QuickChart.io. | Community implementation, developed in Python, chart generation service. | | [Mindmap](https://github.com/YuChenSSR/mindmap-mcp-server) | Generates mind maps from input containing Markdown code. | Community implementation, developed in Python, mind map generation. | | [JSON](https://github.com/GongRzhe/JSON-MCP-Server) | JSON processing server, supporting JSONPath queries and various operations. | Community implementation, developed in Python, advanced JSON processing. | --- ### 🛠️ Productivity Tools and Integration (Office, Project Management, etc.) *(Enables AI to use calendars, task management, project management, note-taking tools, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | Official integration for Pipedream, a one-stop connection to 2500+ APIs, integrating 8000+ tools. | Official implementation, developed in Node.js, powerful API/tool integration platform. | | [Zapier](https://zapier.com/mcp) | Official integration for Zapier, instantly connecting AI agents to 8000+ applications. | Official implementation, connects to the Zapier ecosystem. | | [Make](https://github.com/integromat/make-mcp-server) | Official integration for Make, converting Make scenarios into tools callable by AI assistants. | Official implementation, developed in TypeScript, connects to the Make ecosystem. | | [Fibery](https://github.com/Fibery-inc/fibery-mcp-server) | Official integration for Fibery, executing queries and entity operations within the Fibery workspace. | Official implementation, developed in TypeScript, Fibery work management. | | [Dart](https://github.com/its-dart/dart-mcp-server) | Official integration for Dart, interacting with tasks, documents, and project data in AI-native project management tools. | Official implementation, developed in TypeScript, Dart project management. | | [Airtable (by domdomegg)](https://github.com/domdomegg/airtable-mcp-server) | Reads and writes to Airtable databases, with schema checking. | Community implementation, developed in TypeScript, Airtable read/write. | | [Notion (by v-3)](https://github.com/v-3/notion-server) | Notion integration, allowing Claude to search, read, update, and create pages. | Community implementation, developed in TypeScript, Notion page management. | | [Linear (by jerhadf)](https://github.com/jerhadf/linear-mcp-server) | Interacts with the Linear API for project management, including searching, creating, and updating Issues. | Community implementation, developed in TypeScript, Linear project management. | | [Todoist](https://github.com/abhiz123/todoist-mcp-server) | Interacts with Todoist to manage your tasks. | Community implementation, developed in Python, Todoist task management. | | [Home Assistant (by tevonsb)](https://github.com/tevonsb/homeassistant-mcp) | Interacts with Home Assistant to view and control smart home devices like lights, switches, sensors, etc. | Community implementation, developed in TypeScript, smart home control. | | [Google Tasks](https://github.com/zcaceres/gtasks-mcp) | Google Tasks API server. | Community implementation, developed in TypeScript, Google Tasks management. | | [Rember](https://github.com/rember/rember-mcp) | Creates spaced repetition flashcards in Rember, remembering anything learned in chats. | Official implementation, developed in TypeScript, spaced repetition memory tool. | --- ### 🎥 Multimedia and Content Creation *(Enables AI to generate animations, edit videos, process images, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [abhiemj/manim-mcp-server](https://github.com/abhiemj/manim-mcp-server) | Generates animations using Manim, suitable for creating visual content in mathematics and technology. | Community implementation, runs locally, mathematics/technology animations. | | [burningion/video-editing-mcp](https://github.com/burningion/video-editing-mcp) | A video editing tool that supports adding, analyzing, searching, and generating video clips. | Community implementation, developed in Python, video content creation. | | [EverArt](https://github.com/modelcontextprotocol/servers/tree/main/src/everart) | Official reference implementation, using various models for AI image generation. | Official reference, developed in TypeScript, AI image generation. | | [ElevenLabs](https://github.com/mamertofabian/elevenlabs-mcp-server) | Integrates ElevenLabs TTS API, capable of generating complete voiceovers with various voices. | Community implementation, developed in Python, text-to-speech (TTS). | | [Image Generation](https://github.com/GongRzhe/Image-Generation-MCP-Server) | Provides image generation capabilities using the Replicate Flux model. | Community implementation, developed in Python, AI image generation (Replicate). | | [Replicate](https://github.com/deepfates/mcp-replicate) | Searches, runs, and manages machine learning models on Replicate, processing generated images. | Community implementation, developed in TypeScript, Replicate model invocation. | | [YouTube](https://github.com/ZubeidHendricks/youtube-mcp-server) | Comprehensive YouTube API integration for video management, Shorts creation, and analysis. | Community implementation, developed in Python, YouTube management and analysis. | --- ### 🧠 Knowledge, Memory, and RAG *(Enables AI to have long-term memory and answer questions based on specific knowledge bases.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [modelcontextprotocol/server-memory](https://github.com/modelcontextprotocol/servers/tree/main/src/memory) | Official reference implementation, a persistent memory system based on knowledge graphs. | Official reference, developed in TypeScript, knowledge graph memory. | | [Needle](https://github.com/needle-ai/needle-mcp) | Official integration for Needle, providing out-of-the-box production-grade RAG for searching and retrieving your documents. | Official implementation, developed in TypeScript, production-grade RAG. | | [Inkeep](https://github.com/inkeep/mcp-server-python) | Official integration for Inkeep, searching your content based on Inkeep's RAG. | Official implementation, developed in Python, Inkeep RAG search. | | [Graphlit](https://github.com/graphlit/graphlit-mcp-server) | Official integration for Graphlit, ingesting content from various sources (Slack, Gmail, podcasts, etc.) into a searchable Graphlit project. | Official implementation, developed in TypeScript, multi-source content RAG. | | [Basic Memory](https://github.com/basicmachines-co/basic-memory) | A local-first knowledge management system that builds a semantic graph from Markdown files, achieving persistent memory across conversations. | Community implementation, developed in TypeScript, local Markdown knowledge graph memory. | | [cognee-mcp](https://github.com/topoteretes/cognee/tree/main/cognee-mcp) | GraphRAG memory server, supporting custom ingestion, data processing, and search. | Community implementation, developed in TypeScript, GraphRAG memory. | | [Minima](https://github.com/dmayboroda/minima) | MCP server for local file RAG. | Community implementation, developed in Python, local file RAG. | | [Rememberizer AI](https://github.com/skydeckai/mcp-server-rememberizer) | Interacts with Rememberizer data sources to facilitate enhanced knowledge retrieval. | Community implementation, developed in Python, knowledge retrieval. | --- ### 🔒 Security and Analysis *(Enables AI to perform security scans, binary analysis, risk assessments, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [13bm/GhidraMCP](https://github.com/13bm/GhidraMCP) | Integrates Ghidra for binary analysis, supporting function checks, decompilation, etc. | Community implementation, Python+Java, runs locally, binary reverse engineering. | | [Semgrep](https://github.com/semgrep/mcp) | Official integration for Semgrep, allowing AI agents to use Semgrep for code security scanning. | Official implementation, developed in Python, code security scanning. | | [OpenCTI](https://github.com/Spathodea-Network/opencti-mcp) | Interacts with the OpenCTI platform to retrieve threat intelligence data (reports, indicators, malware, etc.). | Community implementation, developed in Python, threat intelligence retrieval. | | [Heurist Mesh Agent](https://github.com/heurist-network/heurist-mesh-mcp-server) | Accesses professional Web3 AI agents in the Heurist Mesh network for blockchain analysis, smart contract security, token metrics, etc. | Official implementation, developed in Python, Web3 security and analysis. | | [BICScan](https://github.com/ahnlabio/bicscan-mcp) | Retrieves risk scores/assets held for EVM blockchain addresses (EOA, CA, ENS) or even domains. | Official implementation, developed in Python, blockchain address risk analysis. | | [Whois MCP](https://github.com/bharathvaj-ganesan/whois-mcp) | Performs whois queries on domains, IPs, ASNs, and TLDs. | Community implementation, developed in Python, Whois queries. | --- ### 🛠️ Other Useful Tools and Integrations *(Includes calculators, API integrations, specific platform tools, etc.)* | Name | Chinese Introduction | Remarks | | :------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------- | | [AgentRPC](https://github.com/agentrpc/agentrpc) | Official integration for AgentRPC, connecting functions of any language across network boundaries. | Official implementation, Go/Python/TS/Rust, cross-language function calls. | | [APIMatic MCP](https://github.com/apimatic/apimatic-validator-mcp) | Official integration for APIMatic, using APIMatic to validate OpenAPI specifications. | Official implementation, developed in C#, OpenAPI specification validation. | | [IBM wxflows](https://github.com/IBM/wxflows/tree/main/examples/mcp/javascript) | Official tool platform from IBM for building, testing, and deploying tools for any data source. | Official implementation (IBM), developed in JavaScript, general tool platform. | | [Langfuse Prompt Management](https://github.com/langfuse/mcp-server-langfuse) | Official integration for Langfuse, an open-source tool for collaborative editing, version control, evaluation, and publishing prompts. | Official implementation, developed in TypeScript, prompt management. | | [UnifAI](https://github.com/unifai-network/unifai-mcp-server) | Official integration for UnifAI, dynamically searching and invoking tools using the UnifAI network. | Official implementation, developed in Go, dynamic tool discovery and invocation. | | [VeyraX](https://github.com/VeyraX/veyrax-mcp) | Official integration for VeyraX, a single tool control for 100+ API integrations and UI components. | Official implementation, developed in Go, large-scale API/UI control. | | [Calculator](https://github.com/githejie/mcp-server-calculator) | Enables LLM to use a calculator for precise numerical calculations. | Community implementation, developed in Python, basic calculator functionality. | | [Time](https://github.com/modelcontextprotocol/servers/tree/main/src/time) | Official reference implementation, providing time and timezone conversion capabilities. | Official reference, developed in TypeScript, time/zone tools. | | [Sequential Thinking](https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking) | Official reference implementation, solving dynamic and reflective problems through sequential thinking. | Official reference, developed in TypeScript, complex problem-solving framework. | | [OpenAPI AnyApi](https://github.com/baryhuang/mcp-server-any-openapi) | Interacts with large OpenAPI documents using built-in semantic search, customizable prefixes. | Community implementation, developed in Python, large OpenAPI interaction. | | [OpenAPI Schema](https://github.com/hannesj/mcp-openapi-schema) | Allows LLM to explore large OpenAPI schemas without increasing context. | Community implementation, developed in TypeScript, large OpenAPI schema exploration. | | [GraphQL Schema](https://github.com/hannesj/mcp-graphql-schema) | Allows LLM to explore large GraphQL schemas without increasing context. | Community implementation, developed in TypeScript, large GraphQL schema exploration. | --- ## MCP Resources Want to master MCP? These resources will save you time: - **Official Documentation** - [MCP Official Website](https://www.claudemcp.com/) - [Anthropic MCP Introduction](https://www.anthropic.com/news/model-context-protocol) - **Open Source Documentation** - [awesome-mcp-clients](https://github.com/punkpeye/awesome-mcp-clients) - [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-servers) - [Web Directory](https://glama.ai/mcp/servers). - **Community Resources** - [GitHub MCP Repository](https://github.com/anthropic/model-context-protocol): Official code and examples. - [Reddit r/mcp](https://www.reddit.com/r/mcp/): Player discussions, find inspiration. - [Discord](https://glama.ai/mcp/discord): Real-time discussions, problem-solving. - **Tutorials** - [MCP Quick Start](https://glama.ai/blog/2024-11-25-model-context-protocol-quickstart) - [Using SQLite with Claude Desktop](https://youtu.be/wxCCzo9dGj0) - **MCP Analysis Materials** - [a16z In-depth Analysis of MCP](https://a16z.com/a-deep-dive-into-mcp-and-the-future-of-ai-tooling/) - [Comparison of MCP and ANP](https://github.com/agent-network-protocol/AgentNetworkProtocol/blob/main/blogs/cn/MCP%E4%B8%8EANP%E5%AF%B9%E6%AF%94%EF%BC%9A%E6%99%BA%E8%83%BD%E4%BD%93%E9%9C%80%E8%A6%81%E4%BB%80%E4%B9%88%E6%A0%B7%E7%9A%84%E9%80%9A%E4%BF%A1%E5%8D%8F%E8%AE%AE.md) ## MCP Server Development ### **1. Building MCP Servers Using LLM** We can use large language models (LLMs) like Claude to accelerate MCP development! How to use LLMs to build custom Model Context Protocol (MCP) servers and clients? Taking Claude as an example, other large models (GPT, Gemini, Grok, Qwen, DeepSeek) are also applicable. #### **Prepare Documentation** Before starting, please gather the necessary documentation to help Claude understand MCP: 1. Visit [https://modelcontextprotocol.io/llms-full.txt](https://modelcontextprotocol.io/llms-full.txt) and copy the complete document text. 2. Go to the code repositories of [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk) or [Python SDK](https://github.com/modelcontextprotocol/python-sdk). 3. Copy the README file and other relevant documents. 4. Paste these documents into your conversation with Claude. #### **Describe Your Server Requirements** After providing the documentation, clearly describe to Claude what kind of server you want to build. Be specific about: * What resources your server will **open** * What **tools** it will **provide** * What **prompts** it should **offer** * What **external systems** it needs to **interact** with For example: ``` Build an MCP server with the following requirements: - Connect to my company's PostgreSQL database - Open the table structure as a resource - Provide a tool for running read-only SQL queries - Include prompts for common data analysis tasks ``` #### 2. More MCP Programming Resources - [Model Context Protocol (MCP) Programming Quick Start Guide](http://github.com/liaokongVFX/MCP-Chinese-Getting-Started-Guide) --- ## Star History [](https://www.star-history.com/#yzfly/Awesome-MCP-ZH&Date) --- ## Contribution Guidelines Want to contribute? Welcome! - Fork the project, make changes, and submit a PR. - Have new servers or tutorials? Just add them in. --- ## License This project is licensed under the MIT License, free to use and modify, please retain the copyright notice. Copyright (c) 2025 Awesome-MCP-ZH Contributors ---