github-talent-mcp

github-talent-mcp

MCP server that searches, scores, and ranks GitHub developers for technical recruiting.

Category
访问服务器

README

github-talent-mcp

License: MIT Python 3.14+ MCP Claude GitHub API

MCP server that searches, scores, and ranks GitHub developers for technical recruiting.

Demo

https://github.com/user-attachments/assets/2dfd82b4-3eb5-4f2b-bc0a-2580b95043e4

Profile deep dive

Get the full developer profile and activity score for torvalds on GitHub

Claude calls get_developer_profile("torvalds") and returns:

Field Value
Activity Score 150 (reputation floor applied)
Location Portland, OR
Followers 293,321
Stars Received 235,068
Primary Language C (98.1%)
Commits (90d) 0
PRs (90d) 0
Notable Repos linux (183K stars), libdc-for-dirk, subsurface-for-dirk, uemacs, pesern-resolve
Profile README No
Hireable No

Torvalds has zero recent GitHub activity because kernel development flows through mailing lists, not GitHub PRs. The reputation floor (293K followers) overrides the behavioral score and sets it to 150.

Repo contributor ranking

Get the top contributors to huggingface/transformers and rank them for a founding ML engineer role at an AI startup

Claude calls get_repo_contributors("huggingface/transformers")rank_candidates on the top 24 contributors:

Rank Developer Combined Score Activity Relevance Strengths
1 stas00 83.4 150 72 4,553 stars, contributes to major OSS, MIT-licensed repos
2 cyyever 80.8 120 64 1,217 followers, active contributor, profile README
3 Cyrilvallez 77.2 120 56 Active: 13 commits + 57 PRs in 90 days, strong OSS presence
4 ArthurZucker 74.4 120 48 37 PRs in 90 days, contributes to huggingface/transformers
5 ydshieh 72.0 120 40 Active: 9 commits + 40 PRs in 90 days

Combined score = activity × 0.4 + relevance × 0.6. Relevance is keyword overlap with the job description (ML, AI, startup, engineer, etc.).

Installation

1. Clone and install

git clone https://github.com/carolinacherry/github-talent-mcp.git
cd github-talent-mcp
python3 -m venv .venv && source .venv/bin/activate
pip install -e .

2. Create a GitHub personal access token

Go to github.com/settings/tokens and create a fine-grained or classic token with these scopes:

Scope Why
read:user Read user profiles and search users
public_repo Read public repo data, languages, contributors

Create a .env file in the project root:

GITHUB_TOKEN=ghp_xxxxxxxxxxxx

3. Connect to Claude

Claude Code (CLI)

One command:

claude mcp add github-talent -- /path/to/github-talent-mcp/.venv/bin/python3 -m github_talent_mcp

Then set the token as an environment variable. Either:

  • Export it in your shell: export GITHUB_TOKEN=ghp_xxxxxxxxxxxx
  • Or keep it in the .env file — the server reads it via python-dotenv on startup

Restart Claude Code to pick up the new server. Verify with /mcp — you should see 8 tools under github-talent.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "github-talent": {
      "command": "/path/to/github-talent-mcp/.venv/bin/python3",
      "args": ["-m", "github_talent_mcp"],
      "cwd": "/path/to/github-talent-mcp",
      "env": {
        "GITHUB_TOKEN": "ghp_xxxxxxxxxxxx"
      }
    }
  }
}

Restart Claude Desktop. The tools will appear in the toolbox icon.

Try It

Once installed, paste these prompts to verify everything works:

Basic search:

Find Python developers in Raleigh active in the last 60 days

Profile deep dive:

Get the full developer profile and activity score for torvalds on GitHub

Full workflow:

Find 10 ML engineers in San Francisco active in the last 30 days, then rank them for a senior LLM inference engineer role

Repo contributors:

Get the top contributors to huggingface/transformers and rank them for a founding ML engineer role at an AI startup

JD scoring:

Score these candidates against this job description: [paste JD]. Candidates: tiangolo, karpathy, hwchase17

Compare candidates:

Compare tiangolo and hwchase17 for a Senior Python AI Engineer role

Bulk scoring:

Score these 10 GitHub usernames and give me a ranked table: [paste list]

Outreach:

Generate a casual recruiter message for tiangolo about a Senior Python role at Acme. My name is Daniel.

Tools

Tool Description
search_developers Search GitHub users by language, location, activity, followers. For topic-based sourcing, use get_repo_contributors on relevant repos instead.
get_developer_profile Deep profile enrichment: languages, stars, commits + PRs, OSS contributions, license breakdown, profile README, and activity score with breakdown.
rank_candidates Rank usernames against a job description. Returns sorted candidates with combined score, strengths, gaps, and reasoning.
score_against_jd Score candidates against a JD with per-dimension breakdown (tech stack, experience level, OSS signal, leadership). Returns gaps and personalized interview questions.
compare_candidates Side-by-side comparison of 2-5 candidates. Shows dimension winners and a recommendation. Optionally scored against a JD.
bulk_score Score up to 100 GitHub usernames in one call. Returns a ranked markdown table or CSV. Supports optional JD matching.
generate_outreach Generate personalized recruiter messages (short/medium/detailed) that reference the candidate's actual repos and contributions. Requires your company name and sender name. Casual or formal tone.
get_repo_contributors Top contributors for any repo. Accepts owner/repo or full URL. The fastest way to source for a specific domain.

Scoring

The activity score combines two layers: behavioral signals (what you did recently) and a reputation floor (what you've built over time).

Behavioral Score (0-205)

Signal Max Points How
Commits + PRs (last 90 days) 60 Push commits + PR opens (PRs weighted x3). Captures both push-based and PR-based workflows.
Stars on repos 40 Personal repo stars + stars on repos you contribute to. Org repo maintainers get credit.
Profile README 20 Presence of a profile README (github.com/username/username).
Followers 20 Capped at 20.
Repos with descriptions 20 Ratio of repos that have descriptions. Signal of care and polish.
Permissive license repos 15 Has at least one repo with MIT, Apache-2.0, BSD, ISC, or Unlicense.
Major OSS contributions 30 PRs, pushes, or issues on repos you don't own. Capped at 3 repos (10 pts each).

Reputation Floor

The behavioral score alone penalizes developers whose work doesn't produce GitHub events — Torvalds works through mailing lists, senior maintainers merge via org bots, and many engineers work in private repos.

The reputation floor ensures cumulative impact isn't erased by a quiet quarter:

Threshold Floor
10K+ followers or 50K+ stars 150
1K+ followers or 5K+ stars 120
500+ followers or 1K+ stars 100
100+ followers or 200+ stars 80

The final score is max(behavioral_score, reputation_floor). If the floor is applied, the breakdown includes a reputation_floor field so you know.

Score Tiers

  • 150+ — exceptional (top OSS maintainers, well-known engineers)
  • 120-149 — strong signal, worth reaching out
  • 80-119 — solid developer with meaningful public work
  • 40-79 — active but limited public signal
  • <40 — low signal (likely private work or junior)

Ranking

rank_candidates combines the activity score with a relevance score (0-100) based on keyword overlap between the job description and the candidate's profile (bio, languages, repo topics, README). The combined score weights relevance at 60% and activity at 40% — a high-activity developer with no overlap to the job shouldn't outrank a relevant one.

Rate Limits

GitHub REST API: 5,000 requests/hour with token. A typical workflow (search + enrich 5 candidates + rank) uses ~60-100 API calls. Profile results are cached within a session to avoid redundant calls during ranking.

License

MIT

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