Zenodo MCP
About
Tool-based LLM integration with Zenodo via the Model Context Protocol (MCP)
Explore
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
Choose the implementation that best fits your needs:
pip install -r requirements.txt
cp .env.example .env
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:
1. MCP SDK Core (/mcp_sdk_core): A Python-based MCP server implementation designed for integration with Cursor IDE and other MCP-enabled environments.
2. 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
```bash
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