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!)

# 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 for comprehensive UV documentation.

Option 2: Using pip (Traditional)

# 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)

# With UV (recommended)
./scripts/uv_start_server.sh

# Or with traditional Python
python scripts/start_server.py

Testing with MCP Inspector

# 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 2 - Enhanced Tools (Starting Visualization)

Phase 1 - Foundation ✅ COMPLETE

• Project structure created

• Configuration files set up

• Mock data system implemented (8 CPI series, 114 data points)

• Core MCP server implemented with stdio transport

• Basic tools implemented (get_series, list_series, get_series_info)

• 17 unit tests written (all passing)

• UV package manager integration

• SSE transport + ngrok support (bonus!)

• Claude Desktop integration guide

Phase 2 - Enhanced Tools (In Progress)

• Simple visualization tool (static plots) - plot_series tool

• Advanced analysis tools

• Data comparison tools

Available Tools

Phase 1 Tools - Data Access

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:

{
  "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

Phase 2 Tools - Data Formatting for Visualization

plot_series

Get CPI All Items (CUUR0000SA0) data formatted for client-side plotting.

Features:

• Returns structured time series data ready for plotting

• No parameters needed - hardcoded to CPI All Items

• Includes statistics (min, max, average)

• Chronologically sorted data

• Plot instructions for client-side rendering

Parameters:

• None required

Returns:

• data: Array of {date, value, year, month, period} objects

• statistics: {count, min, max, average}

• date_range: {start, end}

• plot_instructions: Suggested chart settings

• series_title: Full series name

Example:

{
  "name": "plot_series",
  "arguments": {}
}

Example Response:

{
  "status": "success",
  "series_id": "CUUR0000SA0",
  "series_title": "Consumer Price Index for All Urban Consumers: All Items",
  "data": [
    {"date": "2020-01", "value": 257.971, "year": "2020", "month": "01", "period": "M01"},
    ...
  ],
  "statistics": {
    "count": 60,
    "min": 257.971,
    "max": 314.540,
    "average": 285.234
  },
  "date_range": {
    "start": "2020-01",
    "end": "2024-12"
  },
  "plot_instructions": {
    "chart_type": "line",
    "x_axis": "date",
    "y_axis": "value",
    "title": "Consumer Price Index for All Urban Consumers: All Items",
    "x_label": "Date",
    "y_label": "Index Value"
  }
}

Note: This tool returns data for client-side plotting, not pre-rendered images. The client (ChatGPT, Claude, etc.) can use this data to create charts in their own environment.

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

# Run all tests
pytest

# Run with coverage
pytest --cov=bls_mcp

# Run specific test file
pytest tests/test_tools.py

Code Quality

# 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 ✅ COMPLETE

• Project setup and configuration

• Mock data system

• Core MCP server with stdio transport

• Basic tools (get_series, list_series, get_series_info)

• Unit tests (17 tests, all passing)

• UV package manager integration

• SSE transport implementation (bonus!)

• ngrok integration (bonus!)

• Claude Desktop integration guide

Phase 2: Enhanced Tools (In Progress)

• Visualization tools (simple static plots)

• Data comparison and analysis tools

• Multi-LLM client testing

• Enhanced error handling and validation

Phase 3: Advanced Features

• MCP resources (catalogs, documentation)

• Pre-built prompts for analysis

• Advanced visualization (interactive charts)

• Migration path to real BLS data

Configuration

Create a .env file (copy from .env.example):

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 - Comprehensive BLS data toolkit (parent project)

• Model Context Protocol - 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.