ollama-mcp-bridge

# MCP-LLM Bridge A TypeScript implementation that connects local LLMs (via Ollama) to Model Context Protocol (MCP) servers. This bridge allows open-source models to use the same tools and capabilities as Claude, enabling powerful local AI assistants. ## Overview This project bridges local Large Language Models with MCP servers that provide various capabilities like: - Filesystem operations - Brave web search - GitHub interactions - Google Drive & Gmail integration - Memory/storage - Image generation with Flux The bridge translates between the LLM's outputs and the MCP's JSON-RPC protocol, allowing any Ollama-compatible model to use these tools just like Claude does. ## Current Setup - **LLM**: Using Qwen 2.5 7B (qwen2.5-coder:7b-instruct) through Ollama - **MCPs**: - Filesystem operations (`@modelcontextprotocol/server-filesystem`) - Brave Search (`@modelcontextprotocol/server-brave-search`) - GitHub (`@modelcontextprotocol/server-github`) - Memory (`@modelcontextprotocol/server-memory`) - Flux image generation (`@patruff/server-flux`) - Gmail & Drive (`@patruff/server-gmail-drive`) ## Architecture - **Bridge**: Core component that manages tool registration and execution - **LLM Client**: Handles Ollama interactions and formats tool calls - **MCP Client**: Manages MCP server connections and JSON-RPC communication - **Tool Router**: Routes requests to appropriate MCP based on tool type ### Key Features - Multi-MCP support with dynamic tool routing - Structured output validation for tool calls - Automatic tool detection from user prompts - Robust process management for Ollama - Detailed logging and error handling ## Setup 1. Install Ollama and required model: ```bash ollama pull qwen2.5-coder:7b-instruct ``` 2. Install MCP servers: ```bash npm install -g @modelcontextprotocol/server-filesystem npm install -g @modelcontextprotocol/server-brave-search npm install -g @modelcontextprotocol/server-github npm install -g @modelcontextprotocol/server-memory npm install -g @patruff/server-flux npm install -g @patruff/server-gmail-drive ``` 3. Configure credentials: - Set `BRAVE_API_KEY` for Brave Search - Set `GITHUB_PERSONAL_ACCESS_TOKEN` for GitHub - Set `REPLICATE_API_TOKEN` for Flux - Run Gmail/Drive MCP auth: `node path/to/gmail-drive/index.js auth` - For example node C:\Users\patru\AppData\Roaming\npm\node_modules\@patruff\server-gmail-drive\dist\index.js auth ## Configuration The bridge is configured through `bridge_config.json`: - MCP server definitions - LLM settings (model, temperature, etc.) - Tool permissions and paths Example: ```json { "mcpServers": { "filesystem": { "command": "node", "args": ["path/to/server-filesystem/dist/index.js"], "allowedDirectory": "workspace/path" }, // ... other MCP configurations }, "llm": { "model": "qwen2.5-coder:7b-instruct", "baseUrl": "http://localhost:11434" } } ``` ## Usage 1. Start the bridge: ```bash npm run start ``` 2. Available commands: - `list-tools`: Show available tools - Regular text: Send prompts to the LLM - `quit`: Exit the program Example interactions: ``` > Search the web for "latest TypeScript features" [Uses Brave Search MCP to find results] > Create a new folder called "project-docs" [Uses Filesystem MCP to create directory] > Send an email to user@example.com [Uses Gmail MCP to compose and send email] ``` ## Technical Details ### Tool Detection The bridge includes smart tool detection based on user input: - Email operations: Detected by email addresses and keywords - Drive operations: Detected by file/folder keywords - Search operations: Contextually routed to appropriate search tool ### Response Processing Responses are processed through multiple stages: 1. LLM generates structured tool calls 2. Bridge validates and routes to appropriate MCP 3. MCP executes operation and returns result 4. Bridge formats response for user ## Extended Capabilities This bridge effectively brings Claude's tool capabilities to local models: - Filesystem manipulation - Web search and research - Email and document management - Code and GitHub interactions - Image generation - Persistent memory All while running completely locally with open-source models. ## Future Improvements - Add support for more MCPs - Implement parallel tool execution - Add streaming responses - Enhance error recovery - Add conversation memory - Support more Ollama models ## Related Projects This bridge integrates with the broader Claude ecosystem: - Model Context Protocol (MCP) - Claude Desktop Configuration - Ollama Project - Various MCP server implementations The result is a powerful local AI assistant that can match many of Claude's capabilities while running entirely on your own hardware.