OpenCollab MCP

OpenCollab MCP

Enables developers to find personalized open-source contributions by analyzing GitHub profiles and matching them with relevant 'good first issues' and beginner-friendly repositories. Provides comprehensive contribution tooling including repository health scoring, setup difficulty assessment, impact estimation, and automated PR planning.

Category
Visit Server

README

OpenCollab MCP

AI-powered open source contribution matchmaker — finds perfect "good first issues" matched to YOUR skills.

Stop scrolling through random issues. Let AI analyze your GitHub profile and find contributions you're actually qualified for, in repos that are actually maintained.


What it does

Tool What it does
opencollab_analyze_profile Analyzes your GitHub profile — languages, topics, contribution patterns
opencollab_find_issues Finds "good first issue" / "help wanted" issues matched to your skills
opencollab_repo_health Scores a repo's contributor-friendliness (0–100)
opencollab_contribution_readiness Checks setup difficulty — Dockerfile, CI, docs, templates
opencollab_generate_pr_plan Gathers full issue context so AI can draft a PR plan
opencollab_trending_repos Finds trending repos actively seeking contributors
opencollab_impact_estimator Estimates contribution impact — stars, reach, resume line

Quick start

1. Get a GitHub token (free)

Go to github.com/settings/tokensGenerate new token (classic) → select public_repo scope → copy the token.

2. Install in Claude Desktop

Add this to your Claude Desktop config:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "opencollab": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/PrakharPandey/opencollab-mcp.git", "opencollab-mcp"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

Restart Claude Desktop. Done!

3. Install in Cursor / VS Code

Add to .cursor/mcp.json or VS Code MCP config:

{
  "mcpServers": {
    "opencollab": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/PrakharPandey/opencollab-mcp.git", "opencollab-mcp"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

4. Alternative: Install with pip

pip install git+https://github.com/PrakharPandey/opencollab-mcp.git

Then use opencollab-mcp as the command (no uvx needed):

{
  "mcpServers": {
    "opencollab": {
      "command": "opencollab-mcp",
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

Example conversations

"Analyze my profile and find me issues"

You: Analyze my GitHub profile (username: prakhar9999) and then find me beginner Python issues in AI/ML projects.

Claude: analyzes profile → finds matching issues → ranks by relevance

"Is this repo good to contribute to?"

You: Check if langchain-ai/langchain is a good repo to contribute to.

Claude: Health score: 85/100. Very active — last push 2 days ago, 72% PR merge rate, has CONTRIBUTING.md...

"Help me plan a PR"

You: I want to work on this issue: https://github.com/org/repo/issues/123. Generate a PR plan.

Claude: fetches issue, comments, repo structure → generates step-by-step plan

"What's the impact?"

You: How impactful would it be to contribute to facebook/react?

Claude: Impact tier: MASSIVE. 230k+ stars. Suggested resume line: "Contributed to a project used by tens of thousands of developers"


Development

# Clone
git clone https://github.com/PrakharPandey/opencollab-mcp.git
cd opencollab-mcp

# Install in development mode
pip install -e .

# Set your token
export GITHUB_TOKEN="your_token_here"

# Run directly
python -m opencollab_mcp.server

# Test with MCP Inspector
npx @modelcontextprotocol/inspector python -m opencollab_mcp.server

How it works

User asks Claude → Claude calls OpenCollab tools → Tools fetch GitHub API → Data returns to Claude → Claude gives smart recommendations

The MCP server is a data bridge, not an AI. It fetches and structures data from GitHub's free API. Claude (which the user already has) does all the intelligent analysis. This means:

  • Zero AI costs for you or your users
  • No API keys needed besides a free GitHub token
  • Works offline (STDIO transport, runs locally)

Requirements

  • Python 3.10+
  • A free GitHub Personal Access Token with public_repo scope
  • Any MCP-compatible client (Claude Desktop, Cursor, VS Code, etc.)

Contributing

Contributions welcome! This project is itself a good first contribution target. Check the issues tab for tasks labeled good first issue.

License

MIT — see LICENSE.


Built by Prakhar Pandey — IIT Guwahati | AI Engineer

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured