TL;DR: I've built an open-source Model Context Protocol (MCP) server that enables AI assistants to search, retrieve, and analyze EA Forum content. This tool can help researchers quickly synthesize EA insights, identify research gaps, and create more informed content. It's available now on GitHub with full documentation.

What is this?

The EA Forum MCP Server is a bridge between AI language models and the EA Forum's vast repository of research, making thousands of EA posts instantly accessible to AI assistants like Claude Desktop, Cursor , and others that support the Model Context Protocol.

Key capabilities:

• Full-text search across all EA Forum posts
• Tag-based filtering for specific cause areas (AI safety, global health, biosecurity, etc.)
• Complete post retrieval with full content and metadata
• Date filtering to find recent developments or historical perspectives
• Built-in caching and retry logic for reliable performance

Why does this matter?

The EA community has produced an enormous wealth of research, analysis, and insights over the years. However, this knowledge can be difficult to systematically search and synthesize, especially when trying to:

• Research existing work before starting a new project
• Find relevant evidence to support grant applications or research proposals
• Identify research gaps by understanding what's already been covered
• Create literature reviews spanning multiple cause areas
• Stay updated on recent developments in specific fields

This tool addresses these challenges by enabling AI assistants to rapidly search, retrieve, and analyze EA Forum content at scale.

Real-world applications

Here are some ways this tool could be valuable:

For researchers:

• Generate comprehensive literature reviews on EA topics
• Identify unexplored research directions by analyzing gaps in existing content
• Find relevant citations and evidence for academic papers or grant applications

For content creators:

• Research existing posts before writing to avoid duplication and build on prior work
• Find successful content formats to emulate (like year-end reviews or cause area summaries)
• Discover counter-arguments and different perspectives on controversial topics

For grantmakers and organizations:

• Quickly assess the landscape of work in a particular cause area
• Find promising researchers or projects mentioned in forum discussions
• Track sentiment and community reception of different approaches

Getting started

The server is open source and available on GitHub with comprehensive documentation and examples. It includes:

• Easy setup with Python virtual environments
• Interactive test scripts for exploring functionality
• Detailed examples showing research workflows
• MCP client configuration for popular AI interfaces

Whether you're a researcher looking to synthesize existing work, a content creator wanting to build on community knowledge, or an organization trying to understand the landscape in your cause area, this tool can help you leverage the collective wisdom of the EA community more effectively.

Community feedback welcome

This is an early-stage project, and I'd love feedback from the community on:

• What additional features would be most valuable?
• What types of analysis or synthesis would you find most helpful?
• How can we make EA knowledge more discoverable and actionable?

The goal is to make EA research more accessible and to help community members build more effectively on each other's work. I'm excited to see how people use this tool and welcome contributions to make it even better.

Acknowledgments

This project was developed as part of my work with Even More Effective, where we're exploring how AI tools can help EA organizations become even more effective. If you're interested in similar projects at the intersection of AI and effective altruism, feel free to reach out!

Links:

• GitHub Repository
• Even More Effective

Note: This tool is designed to complement, not replace, careful reading and engagement with original EA Forum content. It's intended to help with discovery and synthesis, but users should always read original sources and engage thoughtfully with the community.