Content
# 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.
