Unleashing the Power of Open Data: The Model Context Protocol (MCP) Revolution

Empowering LLMs with Real-World Context: A Deep Dive into Open Data Integration

Bridging the Gap: LLMs and the World of Open Data

The Model Context Protocol (MCP) is emerging as a pivotal technology in the evolution of Large Language Models (LLMs). It addresses a fundamental challenge: how to seamlessly integrate LLMs with the vast and ever-growing universe of external data sources and specialized tools. This integration is crucial for transforming LLMs from sophisticated text generators into powerful problem-solving engines capable of reasoning, decision-making, and providing contextually relevant insights.

This initiative focuses on two key pillars:

• Democratizing Open Data Access: Providing a streamlined pathway for LLM applications to tap into a wealth of publicly available datasets.
• Empowering Data Publication: Creating a collaborative ecosystem where data providers can easily contribute and distribute their open data, making it readily discoverable and usable by the broader AI community.

Action in Motion

https://github.com/user-attachments/assets/760e1a16-add6-49a1-bf71-dfbb335e893e

Core Functionality: Access and Publication

1. Accessing Open Data: A Simplified Approach

The Open Data MCP simplifies the process of connecting LLMs to open data sources through a user-friendly CLI tool. This tool enables developers to quickly set up MCP servers within their LLM applications, starting with Claude and expanding to other platforms in the future.

• Effortless Integration: Deploy MCP servers with minimal configuration, enabling instant access to open data.
• CLI-Driven Simplicity: Manage providers and their associated MCP servers through intuitive commands.

2. Publishing Open Data: Contributing to the Ecosystem

The Open Data MCP fosters a collaborative environment where data providers can contribute their datasets and make them accessible to the wider AI community.

• Standardized Templates: Utilize pre-defined templates and guidelines to structure and publish open data in a consistent and easily consumable format.
• Community-Driven Growth: Leverage the collective expertise of the community to expand the range of available datasets and improve data quality.

Practical Implementation: A Step-by-Step Guide

Accessing Open Data via the CLI Tool

Prerequisites

• Claude Desktop App: Ensure the Claude Desktop application is installed (https://claude.ai/download).

• UV Package Manager: Install uv for streamlined CLI and MCP server management.

  • macOS:

    brew install uv

  • Windows:

    powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

CLI Tool Usage

# Display available commands
uvx odmcp

# List available data providers
uvx odmcp list

# Retrieve information about a specific provider
uvx odmcp info $PROVIDER_NAME

# Set up an MCP server for a provider within the Claude Desktop app
uvx odmcp setup $PROVIDER_NAME

# Remove a provider's MCP server
uvx odmcp remove $PROVIDER_NAME

Example: Accessing Swiss Railway Data (SBB)

# Ensure Claude is installed
uvx odmcp setup ch_sbb

After restarting Claude, a new hammer icon will appear in the bottom right corner of the chat interface. You can now query Claude about SBB train network disruptions, and it will respond based on real-time data from data.sbb.ch.

Publishing Open Data: A Detailed Walkthrough

Prerequisites

1. Install UV Package Manager:

   # macOS
   brew install uv
   
   # Windows (PowerShell)
   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
   
   # Linux/WSL
   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone and Set Up the Repository:

   git clone https://github.com/OpenDataMCP/OpenDataMCP.git
   cd OpenDataMCP
   uv venv
   source .venv/bin/activate  # Unix/macOS
   .venv\Scripts\activate     # Windows
   uv sync

3. Install Pre-commit Hooks:

   pre-commit install

Publishing Steps

1. Create a New Provider Module:
   • Each data source requires a dedicated Python module.
   • Create a new module in src/odmcp/providers/, following the naming convention {country_code}_{organization}.py (e.g., ch_sbb.py).
   • Use the provided template file as a starting point.
2. Implement Required Components:
   • Define Tools and Resources based on the template structure.
   • Each Tool/Resource should include:
     • A clear description of its purpose.
     • Well-defined input/output schemas using Pydantic models.
     • Robust error handling.
     • Comprehensive documentation strings.
3. Tool vs. Resource:
   • Choose Tool for data requiring:
     • Active querying or computation.
     • Parameter-based filtering.
     • Complex transformations.
   • Choose Resource for data that is:
     • Static or rarely changing.
     • Small enough to be loaded into memory.
     • Simple file-based content.
     • Reference documentation or lookup tables.
   • Refer to the MCP documentation for guidance.
4. Testing:
   • Add tests in the tests/ directory, following existing patterns.
   • Ensure comprehensive test coverage, including basic functionality, edge cases, and error handling.
5. Validation:
   • Test your MCP server using the experimental client: uv run src/odmcp/providers/client.py.
   • Verify all endpoints respond correctly, error messages are helpful, and performance is adequate under typical query loads.

Contributing to the Open Data Revolution

The Open Data MCP is a community-driven initiative with an ambitious roadmap. Your contributions are essential to achieving the goal of making millions of publicly available datasets accessible to all LLM applications.

Join the Community

Connect with fellow developers and data enthusiasts on our Discord server: https://discord.gg/QPFFZWKW

Core Principles for Contribution

1. Simplicity and Maintainability: Prioritize clear, straightforward code with minimal abstractions.
2. Standardization: Adhere to provided templates and guidelines for consistency.
3. Minimal Dependencies: Keep external dependencies to a minimum, favoring single-repository setups.
4. Code Quality: Follow code formatting guidelines (using ruff) and maintain comprehensive test coverage (using pytest).
5. Type Safety: Utilize Python type hints and Pydantic models for API validation and data handling.

Tactical Priorities

• Initial repository setup with guidelines, testing framework, and contribution workflow.
• CI/CD pipeline implementation with automated PyPI releases.
• Provider template development and initial reference implementation.
• Integration of additional open datasets (actively seeking contributors).
• Clear guidelines for choosing between Resources and Tools.
• Scalable repository architecture for long-term growth.
• Expanded MCP SDK parameter support (authentication, rate limiting, etc.).
• Implementation of additional MCP protocol features (prompts, resource templates).
• Support for alternative transport protocols beyond stdio (SSE).
• Deployment of hosted MCP servers for improved accessibility.

Future Directions: The Roadmap

The Open Data MCP is committed to building the open-source infrastructure that will empower all LLMs to access and utilize open data effectively.

Access

• Extend Open Data availability to all LLM applications (beyond Claude).
• Develop a scalable search mechanism for Open Data sources.
• Provide remote access to Open Data via MCP (SSE) with publicly sponsored infrastructure.

Publish

• Expand the collection of Open Data MCP servers to make open data truly accessible.
• Simplify the process of building Open Data MCP servers.

Limitations

• All data served by Open Data MCP servers must be openly licensed.
• Users must comply with the data licenses of the respective data providers.
• Attribution to this project is required in commercial applications.

References

• We acknowledge and appreciate Anthropic's open-source MCP release, which has enabled initiatives like this one.