Open Data Model Context Protocol
Connect any Open Data to any LLM with Model Context Protocol.
Overview
What is OpenDataMCP?
OpenDataMCP is an innovative platform designed to connect any Open Data to any Large Language Model (LLM) using the Model Context Protocol. This enables seamless integration and utilization of diverse datasets, enhancing the capabilities of machine learning models and facilitating better data-driven decision-making.
Features of OpenDataMCP
- Versatile Data Integration: OpenDataMCP allows users to connect various types of open data sources to LLMs, making it easier to leverage existing datasets for advanced analytics and insights.
- Model Context Protocol: This unique protocol ensures that the data is contextualized appropriately for the LLMs, improving the relevance and accuracy of the outputs generated.
- User-Friendly Interface: The platform is designed with usability in mind, providing an intuitive interface that simplifies the process of data connection and model training.
- Public Repository: OpenDataMCP is available as a public repository, encouraging collaboration and contributions from developers and data scientists worldwide.
How to Use OpenDataMCP
- Access the Repository: Visit the OpenDataMCP GitHub repository to explore the available resources and documentation.
- Set Up Your Environment: Follow the setup instructions provided in the repository to configure your development environment for using OpenDataMCP.
- Connect Your Data: Utilize the Model Context Protocol to link your open data sources with the LLMs. Detailed guidelines are available in the documentation.
- Train Your Model: Once your data is connected, you can begin training your LLM with the integrated datasets, optimizing for your specific use cases.
- Collaborate and Share: Engage with the community by contributing to the repository, sharing your findings, and collaborating on new features or improvements.
Frequently Asked Questions
Q: What types of open data can be connected to OpenDataMCP?
A: OpenDataMCP supports a wide range of open data formats and sources, including CSV, JSON, and APIs from various public datasets.
Q: Is OpenDataMCP free to use?
A: Yes, OpenDataMCP is a public repository and is free to use. Users can access the platform without any subscription fees.
Q: Can I contribute to OpenDataMCP?
A: Absolutely! Contributions are welcome. You can fork the repository, make improvements, and submit pull requests to share your enhancements with the community.
Q: What are the system requirements for using OpenDataMCP?
A: The system requirements may vary based on the specific LLM you are using. However, a standard development environment with Python and necessary libraries is generally sufficient.
Q: How can I stay updated on OpenDataMCP developments?
A: You can follow the repository on GitHub and star it to receive notifications about updates, new features, and community discussions.
Details
Open Data Model Context Protocol
See it in action
https://github.com/user-attachments/assets/760e1a16-add6-49a1-bf71-dfbb335e893e
We enable 2 things:
- Open Data Access: Access to many public datasets right from your LLM application (starting with Claude, more to come).
- Publishing: Get community help and a distribution network to distribute your Open Data. Get everyone to use it!
How do we do that?
- Access: Setup our MCP servers in your LLM application in 2 clicks via our CLI tool (starting with Claude, see Roadmap for next steps).
- Publish: Use provided templates and guidelines to quickly contribute and publish on Open Data MCP. Make your data easily discoverable!
Usage
<u>Access</u>: Access Open Data using Open Data MCP CLI Tool
Prerequisites
If you want to use Open Data MCP with Claude Desktop app client you need to install the Claude Desktop app.
You will also need uv to easily run our CLI and MCP servers.
macOS
### you need to install uv through homebrew as using the install shell script
### will install it locally to your user which make it unavailable in the Claude Desktop app context.
brew install uv
Windows
### (UNTESTED)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Open Data MCP - CLI Tool
Overview
### show available commands
uvx odmcp
### show available providers
uvx odmcp list
### show info about a provider
uvx odmcp info $PROVIDER_NAME
### setup a provider's MCP server on your Claude Desktop app
uvx odmcp setup $PROVIDER_NAME
### remove a provider's MCP server from your Claude Desktop app
uvx odmcp remove $PROVIDER_NAME
Example
Quickstart for the Switzerland SBB (train company) provider:
### make sure claude is installed
uvx odmcp setup ch_sbb
Restart Claude and you should see a new hammer icon at the bottom right of the chat.
You can now ask questions to Claude about SBB train network disruption and it will answer based on data collected on data.sbb.ch.
<u>Publish</u>: Contribute by building and publishing public datasets
Prerequisites
-
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 -
Clone & Setup Repository
# Clone the repository git clone https://github.com/OpenDataMCP/OpenDataMCP.git cd OpenDataMCP # Create and activate virtual environment uv venv source .venv/bin/activate # Unix/macOS # or .venv\Scripts\activate # Windows # Install dependencies uv sync -
Install Pre-commit Hooks
# Install pre-commit hooks for code quality pre-commit install
Publishing Instructions
-
Create a New Provider Module
- Each data source needs its own python module.
- Create a new Python module in
src/odmcp/providers/. - Use a descriptive name following the pattern:
{country_code}_{organization}.py(e.g.,ch_sbb.py). - Start with our template file as your base.
-
Implement Required Components
- Define your Tools & Resources following the template structure
- Each Tool or Resource should have:
- Clear description of its purpose
- Well-defined input/output schemas using Pydantic models
- Proper error handling
- Documentation strings
-
Tool vs Resource
- Choose Tool implementation if your data needs:
- Active querying or computation
- Parameter-based filtering
- Complex transformations
- Choose Resource implementation if your data is:
- Static or rarely changing
- Small enough to be loaded into memory
- Simple file-based content
- Reference documentation or lookup tables
- Reference the MCP documentation for guidance
- Choose Tool implementation if your data needs:
-
Testing
- Add tests in the
tests/directory - Follow existing test patterns (see other provider tests)
- Required test coverage:
- Basic functionality
- Edge cases
- Error handling
- Add tests in the
-
Validation
- Test your MCP server using our experimental client:
uv run src/odmcp/providers/client.py - Verify all endpoints respond correctly
- Ensure error messages are helpful
- Check performance with typical query loads
- Test your MCP server using our experimental client:
For other examples, check our existing providers in the src/odmcp/providers/ directory.
Contributing
We have an ambitious roadmap and we want this project to scale with the community. The ultimate goal is to make the millions of datasets publicly available to all LLM applications.
For that we need your help!
Discord
We want to build a helping community around the challenge of bringing open data to LLM's. Join us on discord to start chatting: https://discord.gg/QPFFZWKW
Our Core Guidelines
Because of our target scale we want to keep things simple and pragmatic at first. Tackle issues with the community as they come along.
-
Simplicity and Maintainability
- Minimize abstractions to keep codebase simple and scalable
- Focus on clear, straightforward implementations
- Avoid unnecessary complexity
-
Standardization / Templates
- Follow provided templates and guidelines consistently
- Maintain uniform structure across providers
- Use common patterns for similar functionality
-
Dependencies
- Keep external dependencies to a minimum
- Prioritize single repository/package setup
- Carefully evaluate necessity of new dependencies
-
Code Quality
- Format code using ruff
- Maintain comprehensive test coverage with pytest
- Follow consistent code style
-
Type Safety
- Use Python type hints throughout
- Leverage Pydantic models for API request/response validation
- Ensure type safety in data handling
Tactical Topics (our current priorities)
- Initialize repository with guidelines, testing framework, and contribution workflow
- Implement CI/CD pipeline with automated PyPI releases
- Develop provider template and first reference implementation
- Integrate additional open datasets (actively seeking contributors)
- Establish clear guidelines for choosing between Resources and Tools
- Develop scalable repository architecture for long-term growth
- Expand MCP SDK parameter support (authentication, rate limiting, etc.)
- Implement additional MCP protocol features (prompts, resource templates)
- Add support for alternative transport protocols beyond stdio (SSE)
- Deploy hosted MCP servers for improved accessibility
Roadmap
Let’s build the open source infrastructure that will allow all LLMs to access all Open Data together!
Access:
- Make Open Data available to all LLM applications (beyond Claude)
- Make Open Data data sources searchable in a scalable way
- Make Open Data available through MCP remotely (SSE) with publicly sponsored infrastructure
Publish:
- Build the many Open Data MCP servers to make all the Open Data truly accessible (we need you!).
- On our side we are starting to build MCP servers for Switzerland ~12k open dataset!
- Make it even easier to build Open Data MCP servers
We are very early, and lack of dataset available is currently the bottleneck. Help yourself! Create your Open Data MCP server and get users to use it as well from their LLMs applications. Let’s connect LLMs to the millions of open datasets from governments, public entities, companies and NGOs!
As Anthropic's MCP evolves we will adapt and upgrade Open Data MCP.
Limitations
- All data served by Open Data MCP servers should be Open.
- Please oblige to the data licenses of the data providers.
- Our License must be quoted in commercial applications.
References
- Kudos to Anthropic's open source MCP release enabling initiative like this one.
License
This project is licensed under the MIT License - see the LICENSE file for details
Server Config
{
"mcpServers": {
"open-data-mcp": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/metorial/mcp-container--opendatamcp--opendatamcp--open-data-mcp",
"odmcp"
],
"env": {}
}
}
}
