vertex-ai-mcp-server

[](https://mseep.ai/app/shariqriazz-vertex-ai-mcp-server) # Vertex AI MCP Server [](https://smithery.ai/server/@shariqriazz/vertex-ai-mcp-server) This project implements a Model Context Protocol (MCP) server that provides a comprehensive suite of tools for interacting with Google Cloud's Vertex AI Gemini models, focusing on coding assistance and general query answering. <a href="https://glama.ai/mcp/servers/@shariqriazz/vertex-ai-mcp-server"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@shariqriazz/vertex-ai-mcp-server/badge" alt="Vertex AI Server MCP server" /> </a> ## Features * Provides access to Vertex AI Gemini models via numerous MCP tools. * Supports web search grounding (`answer_query_websearch`) and direct knowledge answering (`answer_query_direct`). * Configurable model ID, temperature, streaming behavior, max output tokens, and retry settings via environment variables. * Uses streaming API by default for potentially better responsiveness. * Includes basic retry logic for transient API errors. * Minimal safety filters applied (`BLOCK_NONE`) to reduce potential blocking (use with caution). ## Tools Provided ### Query & Generation (AI Focused) * `answer_query_websearch`: Answers a natural language query using the configured Vertex AI model enhanced with Google Search results. * `answer_query_direct`: Answers a natural language query using only the internal knowledge of the configured Vertex AI model. * `explain_topic_with_docs`: Provides a detailed explanation for a query about a specific software topic by synthesizing information primarily from official documentation found via web search. * `get_doc_snippets`: Provides precise, authoritative code snippets or concise answers for technical queries by searching official documentation. * `generate_project_guidelines`: Generates a structured project guidelines document (Markdown) based on a specified list of technologies (optionally with versions), using web search for best practices. ### Research & Analysis Tools * `code_analysis_with_docs`: Analyzes code snippets by comparing them with best practices from official documentation, identifying potential bugs, performance issues, and security vulnerabilities. * `technical_comparison`: Compares multiple technologies, frameworks, or libraries based on specific criteria, providing detailed comparison tables with pros/cons and use cases. * `architecture_pattern_recommendation`: Suggests architecture patterns for specific use cases based on industry best practices, with implementation examples and considerations. * `dependency_vulnerability_scan`: Analyzes project dependencies for known security vulnerabilities, providing detailed information and mitigation strategies. * `database_schema_analyzer`: Reviews database schemas for normalization, indexing, and performance issues, suggesting improvements based on database-specific best practices. * `security_best_practices_advisor`: Provides security recommendations for specific technologies or scenarios, with code examples for implementing secure practices. * `testing_strategy_generator`: Creates comprehensive testing strategies for applications or features, suggesting appropriate testing types with coverage goals. * `regulatory_compliance_advisor`: Provides guidance on regulatory requirements for specific industries (GDPR, HIPAA, etc.), with implementation approaches for compliance. * `microservice_design_assistant`: Helps design microservice architectures for specific domains, with service boundary recommendations and communication patterns. * `documentation_generator`: Creates comprehensive documentation for code, APIs, or systems, following industry best practices for technical documentation. ### Filesystem Operations * `read_file_content`: Read the complete contents of one or more files. Provide a single path string or an array of path strings. * `write_file_content`: Create new files or completely overwrite existing files. The 'writes' argument accepts a single object (`{path, content}`) or an array of such objects. * `edit_file_content`: Makes line-based edits to a text file, returning a diff preview or applying changes. * `list_directory_contents`: Lists files and directories directly within a specified path (non-recursive). * `get_directory_tree`: Gets a recursive tree view of files and directories as JSON. * `move_file_or_directory`: Moves or renames files and directories. * `search_filesystem`: Recursively searches for files/directories matching a name pattern, with optional exclusions. * `get_filesystem_info`: Retrieves detailed metadata (size, dates, type, permissions) about a file or directory. * `execute_terminal_command`: Execute a shell command, optionally specifying `cwd` and `timeout`. Returns stdout/stderr. ### Combined AI + Filesystem Operations * `save_generate_project_guidelines`: Generates project guidelines based on a tech stack and saves the result to a specified file path. * `save_doc_snippet`: Finds code snippets from documentation and saves the result to a specified file path. * `save_topic_explanation`: Generates a detailed explanation of a topic based on documentation and saves the result to a specified file path. * `save_answer_query_direct`: Answers a query using only internal knowledge and saves the answer to a specified file path. * `save_answer_query_websearch`: Answers a query using web search results and saves the answer to a specified file path. *(Note: Input/output schemas for each tool are defined in their respective files within `src/tools/` and exposed via the MCP server.)* ## Prerequisites * Node.js (v18+) * Bun (`npm install -g bun`) * Google Cloud Project with Billing enabled. * Vertex AI API enabled in the GCP project. * Google Cloud Authentication configured in your environment (Application Default Credentials via `gcloud auth application-default login` is recommended, or a Service Account Key). ## Setup & Installation 1. **Clone/Place Project:** Ensure the project files are in your desired location. 2. **Install Dependencies:** ```bash bun install ``` 3. **Configure Environment:** * Create a `.env` file in the project root (copy `.env.example`). * Set the required and optional environment variables as described in `.env.example`. * Set `AI_PROVIDER` to either `"vertex"` or `"gemini"`. * If `AI_PROVIDER="vertex"`, `GOOGLE_CLOUD_PROJECT` is required. * If `AI_PROVIDER="gemini"`, `GEMINI_API_KEY` is required. 4. **Build the Server:** ```bash bun run build ``` This compiles the TypeScript code to `build/index.js`. ## Usage (Standalone / NPX) Once published to npm, you can run this server directly using `npx`: ```bash # Ensure required environment variables are set (e.g., GOOGLE_CLOUD_PROJECT) bunx vertex-ai-mcp-server ``` Alternatively, install it globally: ```bash bun install -g vertex-ai-mcp-server # Then run: vertex-ai-mcp-server ``` **Note:** Running standalone requires setting necessary environment variables (like `GOOGLE_CLOUD_PROJECT`, `GOOGLE_CLOUD_LOCATION`, authentication credentials if not using ADC) in your shell environment before executing the command. ### Installing via Smithery To install Vertex AI Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@shariqriazz/vertex-ai-mcp-server): ```bash bunx -y @smithery/cli install @shariqriazz/vertex-ai-mcp-server --client claude ``` ## Running with Cline 1. **Configure MCP Settings:** Add/update the configuration in your Cline MCP settings file (e.g., `.roo/mcp.json`). You have two primary ways to configure the command: **Option A: Using Node (Direct Path - Recommended for Development)** This method uses `node` to run the compiled script directly. It's useful during development when you have the code cloned locally. ```json { "mcpServers": { "vertex-ai-mcp-server": { "command": "node", "args": [ "/full/path/to/your/vertex-ai-mcp-server/build/index.js" // Use absolute path or ensure it's relative to where Cline runs node ], "env": { // --- General AI Configuration --- "AI_PROVIDER": "vertex", // "vertex" or "gemini" // --- Required (Conditional) --- "GOOGLE_CLOUD_PROJECT": "YOUR_GCP_PROJECT_ID", // Required if AI_PROVIDER="vertex" // "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY", // Required if AI_PROVIDER="gemini" // --- Optional Model Selection --- "VERTEX_MODEL_ID": "gemini-2.5-pro-exp-03-25", // If AI_PROVIDER="vertex" (Example override) "GEMINI_MODEL_ID": "gemini-2.5-pro-exp-03-25", // If AI_PROVIDER="gemini" // --- Optional AI Parameters --- "GOOGLE_CLOUD_LOCATION": "us-central1", // Specific to Vertex AI "AI_TEMPERATURE": "0.0", "AI_USE_STREAMING": "true", "AI_MAX_OUTPUT_TOKENS": "65536", // Default from .env.example "AI_MAX_RETRIES": "3", "AI_RETRY_DELAY_MS": "1000", // --- Optional Vertex Authentication --- // "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/your/service-account-key.json" // If using Service Account Key for Vertex }, "disabled": false, "alwaysAllow": [ // Add tool names here if you don't want confirmation prompts // e.g., "answer_query_websearch" ], "timeout": 3600 // Optional: Timeout in seconds } // Add other servers here... } } ``` * **Important:** Ensure the `args` path points correctly to the `build/index.js` file. Using an absolute path might be more reliable. **Option B: Using NPX (Requires Package Published to npm)** This method uses `npx` to automatically download and run the server package from the npm registry. This is convenient if you don't want to clone the repository. ```json { "mcpServers": { "vertex-ai-mcp-server": { "command": "bunx", // Use bunx "args": [ "-y", // Auto-confirm installation "vertex-ai-mcp-server" // The npm package name ], "env": { // --- General AI Configuration --- "AI_PROVIDER": "vertex", // "vertex" or "gemini" // --- Required (Conditional) --- "GOOGLE_CLOUD_PROJECT": "YOUR_GCP_PROJECT_ID", // Required if AI_PROVIDER="vertex" // "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY", // Required if AI_PROVIDER="gemini" // --- Optional Model Selection --- "VERTEX_MODEL_ID": "gemini-2.5-pro-exp-03-25", // If AI_PROVIDER="vertex" (Example override) "GEMINI_MODEL_ID": "gemini-2.5-pro-exp-03-25", // If AI_PROVIDER="gemini" // --- Optional AI Parameters --- "GOOGLE_CLOUD_LOCATION": "us-central1", // Specific to Vertex AI "AI_TEMPERATURE": "0.0", "AI_USE_STREAMING": "true", "AI_MAX_OUTPUT_TOKENS": "65536", // Default from .env.example "AI_MAX_RETRIES": "3", "AI_RETRY_DELAY_MS": "1000", // --- Optional Vertex Authentication --- // "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/your/service-account-key.json" // If using Service Account Key for Vertex }, "disabled": false, "alwaysAllow": [ // Add tool names here if you don't want confirmation prompts // e.g., "answer_query_websearch" ], "timeout": 3600 // Optional: Timeout in seconds } // Add other servers here... } } ``` * Ensure the environment variables in the `env` block are correctly set, either matching `.env` or explicitly defined here. Remove comments from the actual JSON file. 2. **Restart/Reload Cline:** Cline should detect the configuration change and start the server. 3. **Use Tools:** You can now use the extensive list of tools via Cline. ## Development * **Watch Mode:** `bun run watch` * **Linting:** `bun run lint` * **Formatting:** `bun run format` ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.