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
访问服务器

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

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选