Multi-service MCP Server
by AdamPippert
# MCP Server Implementation Summary
We've built a comprehensive Model Context Protocol (MCP) server that provides a standardized way for AI models to interact with tools and services. This implementation aligns with the [Model Context Protocol standards](https://modelcontextprotocol.io/) and provides a modular architecture for integrating various tools.
## Key Components
1. **MCP Gateway**: A unified entry point that routes requests to the appropriate tool
2. **MCP Manifest**: Provides a standardized description of all available tools and their capabilities
3. **Modular Tool Architecture**: Each tool is implemented as a separate module that can be easily added or removed
4. **Direct API Access**: Each tool can be accessed directly via RESTful API endpoints
5. **Integration with Language Models**: Examples for integrating with OpenAI and Anthropic LLMs
## Implemented Tools
Our MCP server includes five key tools:
1. **GitHub Tool**: For interacting with GitHub repositories, issues, and search
2. **GitLab Tool**: For interacting with GitLab projects, issues, and pipelines
3. **Google Maps Tool**: For geocoding, directions, and places search
4. **Memory Tool**: For persistent storage and retrieval of data
5. **Puppeteer Tool**: For web automation, screenshots, PDFs, and content extraction
## MCP Protocol Compliance
This implementation follows the Model Context Protocol specification by:
1. **Standardized Request Format**:
```json
{
"tool": "github",
"action": "listRepos",
"parameters": {
"username": "octocat"
}
}
```
2. **Standardized Response Format**:
```json
{
"tool": "github",
"action": "listRepos",
"status": "success",
"result": [...]
}
```
3. **Tool Discovery via Manifest**:
- Provides a comprehensive manifest at `/mcp/manifest`
- Documents all tools, actions, parameters, and return types
4. **Error Handling**:
- Consistent error reporting across all tools
- Error responses include type and message
## Modularity and Extensibility
The architecture is designed for modularity and extensibility:
1. **Tool Module Structure**:
- Each tool is contained in its own module
- Modules implement standard interfaces for actions
2. **Adding New Tools**:
- Create a new module file in the `tools` directory
- Implement action handlers and API endpoints
- Register the tool in the MCP manifest
3. **Configuration and Deployment**:
- Environment-based configuration
- Multiple deployment options (direct, container, OpenShift)
- Red Hat specific optimizations
## Integration with LLMs
The MCP server integrates seamlessly with Large Language Models:
1. **OpenAI Integration**:
- Converts MCP tool definitions to OpenAI function calling format
- Handles multi-step interactions with tool calling
2. **Anthropic Integration**:
- Adapts to Anthropic's tool calling format
- Maps between different message formats
3. **Tool Execution**:
- Provides a standardized interface for executing tool actions
- Handles errors and formats responses for the LLM
## Visual Architecture
The modular architecture follows first principles and separates concerns into distinct layers:
1. **Gateway Layer**: Handles routing and protocol compliance
2. **Tool Layer**: Implements specific tool functionality
3. **External Service Layer**: Connects to external APIs and services
## Next Steps and Future Enhancements
Potential enhancements for the MCP server:
1. **Additional Tools**:
- Adding file storage/retrieval tools
- Database interaction tools
- Email sending/receiving tools
2. **Authentication and Authorization**:
- Implementing OAuth for GitHub/GitLab
- Role-based access control for tools
3. **Performance Optimizations**:
- Caching frequently used results
- Connection pooling for external services
4. **Monitoring and Observability**:
- Metrics collection via Prometheus
- Distributed tracing with OpenTelemetry
5. **Streaming Support**:
- Adding support for streaming responses
- WebSocket integration for real-time updates