mcp-zenodo
Enables LangChain agents and tools to interact with Zenodo records through MCP tools.
Supports LangGraph workflows and graphs for integrating Zenodo data into applications.
Provides an OpenAI-compatible API endpoint for integrating Zenodo interactions with OpenAI clients.
Provides tools for searching, retrieving, citing, and downloading files from Zenodo records.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-zenodosearch for records on climate change"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Zenodo MCP
A comprehensive toolkit for interacting with Zenodo records through the Model Context Protocol (MCP), providing two distinct implementations for different use cases.
Repository Structure
This repository contains two main implementations:
MCP SDK Core (
/mcp_sdk_core): A Python-based MCP server implementation designed for integration with Cursor IDE and other MCP-enabled environments.MCP API (
/mcp_api): A FastAPI-based service that provides MCP-compatible tools for integration with LLM frameworks like LangChain and LangGraph.
Implementation Differences
MCP SDK Core
The MCP SDK Core implementation is designed for direct integration with MCP-enabled environments like Cursor IDE. It provides:
Direct MCP Integration: Follows the Model Context Protocol standard developed by Anthropic
Cursor IDE Compatibility: Seamlessly integrates with Cursor's MCP extension
Simple Configuration: Managed through a JSON config file
Unified API: Standardized access to Zenodo resources
This implementation is ideal for developers who want to access Zenodo directly from their development environment without additional middleware.
Learn more about the MCP SDK Core implementation →
MCP API
The MCP API implementation is a FastAPI-based service that provides MCP-compatible tools for integration with LLM frameworks. It offers:
LangChain Integration: Seamless integration with LangChain agents and tools
LangGraph Compatibility: Support for LangGraph workflows and graphs
OpenAI-Compatible API: Can be used with OpenAI-compatible clients
LibreChat Support: Compatible with LibreChat and similar platforms
Custom Tool Creation: Extensible architecture for creating custom tools
This implementation is ideal for developers building LLM applications that need to interact with Zenodo as part of a larger workflow.
Learn more about the MCP API implementation →
Features
Both implementations provide access to Zenodo's rich repository of research outputs:
Search and Retrieve Records: Find and access Zenodo records
Get Citations: Retrieve citations in various formats (BibTeX, APA, etc.)
Detect Data Types: Automatically classify Zenodo records
Access Metadata: Get detailed information about records
List and Download Files: Browse and download files from records
Getting Started
Choose the implementation that best fits your needs:
For Cursor IDE Integration
# Clone the repository
git clone https://github.com/yourusername/zenodo-mcp.git
cd zenodo-mcp/mcp_sdk_core
# Install dependencies
pip install -r requirements.txt
# Configure Cursor IDE (create mcp.json)
# See mcp_sdk_core/README.md for detailsFor LLM Framework Integration
# Clone the repository
git clone https://github.com/yourusername/zenodo-mcp.git
cd zenodo-mcp/mcp_api
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your Zenodo API token
# Run the API server
uvicorn server.main:app --host 0.0.0.0 --port 8000Contributing
We welcome contributions to both implementations! Please see the respective README files for contribution guidelines.
License
This project is licensed under the MIT License - see the LICENSE file for details.
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