BLS MCP Server

# BLS MCP Server A standalone MCP (Model Context Protocol) server for Bureau of Labor Statistics (BLS) data, designed to work with multiple LLM clients through both local and remote connections. ## Features - **Official MCP SDK**: Built with the official `mcp` Python SDK for full protocol control - **Mock Data First**: Uses realistic mock BLS data for rapid development and testing - **Multiple Transports**: Supports both stdio (local) and SSE (remote via ngrok) - **Multi-LLM Compatible**: Test with Claude, GPT-4, and other MCP-compatible clients - **Modular Design**: Clean separation between tools, resources, and data providers ## Quick Start ### Installation #### Option 1: Using UV (Recommended - 10x faster!) ```bash # Install uv (if not already installed) curl -LsSf https://astral.sh/uv/install.sh | sh # Navigate to project cd bls_mcp # Sync dependencies (creates .venv automatically) uv sync # Run the server ./scripts/uv_start_server.sh # Test the server ./scripts/uv_test_client.sh ``` See [UV_USAGE.md](docs/UV_USAGE.md) for comprehensive UV documentation. #### Option 2: Using pip (Traditional) ```bash # Clone the repository cd bls_mcp # Create virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -e . # Or install with dev dependencies pip install -e ".[dev]" ``` ### Running the Server (Local) ```bash # With UV (recommended) ./scripts/uv_start_server.sh # Or with traditional Python python scripts/start_server.py ``` ### Testing with MCP Inspector ```bash # Install MCP inspector (if not already installed) npm install -g @modelcontextprotocol/inspector # Run inspector mcp-inspector python scripts/start_server.py ``` ## Project Status **Current Phase**: Phase 1 - Foundation - [x] Project structure created - [x] Configuration files set up - [ ] Mock data system implemented - [ ] Core MCP server implemented - [ ] Basic tools implemented - [ ] Tests written ## Available Tools (Phase 1) ### `get_series` Fetch BLS data series by ID with optional date range filtering. **Parameters:** - `series_id` (string, required): BLS series ID (e.g., "CUUR0000SA0") - `start_year` (integer, optional): Start year for data range - `end_year` (integer, optional): End year for data range **Example:** ```json { "name": "get_series", "arguments": { "series_id": "CUUR0000SA0", "start_year": 2020, "end_year": 2024 } } ``` ### `list_series` List available BLS series with optional filtering. **Parameters:** - `category` (string, optional): Filter by category (e.g., "CPI", "Employment") - `limit` (integer, optional): Maximum number of results (default: 50) ### `get_series_info` Get detailed metadata about a specific BLS series. **Parameters:** - `series_id` (string, required): BLS series ID ## Architecture ### Directory Structure ``` bls_mcp/ ├── src/bls_mcp/ │ ├── server.py # Main MCP server │ ├── transports/ │ │ ├── stdio.py # stdio transport (local) │ │ └── sse.py # SSE transport (remote - Phase 2) │ ├── tools/ │ │ ├── base.py # Base tool class │ │ ├── get_series.py # Get series tool │ │ ├── list_series.py # List series tool │ │ └── get_series_info.py # Get series info tool │ ├── data/ │ │ ├── mock_data.py # Mock data provider │ │ └── fixtures/ # JSON data fixtures │ └── utils/ │ ├── logger.py # Logging configuration │ └── validators.py # Input validation ├── tests/ # Test suite ├── scripts/ # Utility scripts └── docs/ # Documentation ``` ### Data Flow 1. **Client Request** → MCP protocol (JSON-RPC) 2. **Transport Layer** → stdio or SSE 3. **Server Router** → Route to appropriate tool 4. **Tool Execution** → Fetch data from provider 5. **Data Provider** → Mock or real data source 6. **Response** → JSON formatted response ## Mock Data The server uses realistic mock BLS data that follows the actual BLS API structure: - **CPI Series**: Consumer Price Index data for various categories - **Time Range**: 2020-2024 with monthly data points - **Coverage**: Multiple categories (All Items, Food, Energy, Housing, etc.) - **Realistic Values**: Based on actual BLS data patterns ## Development ### Running Tests ```bash # Run all tests pytest # Run with coverage pytest --cov=bls_mcp # Run specific test file pytest tests/test_tools.py ``` ### Code Quality ```bash # Format code black src/ tests/ # Lint code ruff check src/ tests/ # Type checking mypy src/ ``` ### Adding New Tools 1. Create tool file in `src/bls_mcp/tools/` 2. Implement tool class following the base pattern 3. Register tool in `server.py` 4. Add tests in `tests/test_tools.py` 5. Update documentation ## Roadmap ### Phase 1: Foundation (Current) - [x] Project setup and configuration - [ ] Mock data system - [ ] Core MCP server with stdio transport - [ ] Basic tools (get_series, list_series, get_series_info) - [ ] Unit tests ### Phase 2: Remote Access - [ ] SSE transport implementation - [ ] ngrok integration - [ ] Multi-LLM client testing - [ ] Enhanced tools with visualization ### Phase 3: Advanced Features - [ ] MCP resources (catalogs, documentation) - [ ] Pre-built prompts for analysis - [ ] Advanced analysis tools - [ ] Migration path to real BLS data ## Configuration Create a `.env` file (copy from `.env.example`): ```env MCP_SERVER_PORT=3000 MCP_SERVER_HOST=localhost LOG_LEVEL=INFO DATA_PROVIDER=mock ``` ## Contributing This is a personal project, but suggestions and feedback are welcome! ## License MIT License - see LICENSE file for details ## Related Projects - [bls_data](../bls_data/) - Comprehensive BLS data toolkit (parent project) - [Model Context Protocol](https://modelcontextprotocol.io/) - MCP specification and documentation ## Support For issues or questions, please refer to the documentation in the `docs/` directory or check the PLAN.md file for development details.