mcp-servers (Six production-minded MCP servers)

mcp-servers (Six production-minded MCP servers)

A workspace of six MCP servers—grounded-cv (hybrid RAG with citations), repo-intel (GitHub repo intelligence), web-research (safe web search/extraction), ats-jobs (unified ATS job boards), outcome-ledger (durable task ledger), and coding-workflows (code review & planning)—purpose-built for agentic hiring evaluations.

Category
访问服务器

README

Six production-minded MCP servers

CI Python 3.11–3.13 MCP SDK 2.0

60-second quickstart

git clone https://github.com/prathamesh-git9/mcp-servers.git
cd mcp-servers
uv sync --all-packages --dev
uv run grounded-cv-eval
uv run pytest -q

That runs a real BM25 + dense + reciprocal-rank-fusion evaluation and the entire socket-restricted test suite. Each server is directly runnable with uv run grounded-cv, uv run repo-intel, uv run web-research, uv run ats-jobs, uv run outcome-ledger, or uv run coding-workflows.

claude_desktop_config.json

Copy this complete block. uvx installs the two required workspace packages from the public repository and caches the environment locally.

{
  "mcpServers": {
    "grounded-cv": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/grounded-cv",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "grounded-cv"
      ]
    },
    "repo-intel": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/repo-intel",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "repo-intel"
      ]
    },
    "web-research": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/web-research",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "web-research"
      ]
    },
    "ats-jobs": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/ats-jobs",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "ats-jobs"
      ]
    },
    "outcome-ledger": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/outcome-ledger",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "outcome-ledger"
      ]
    },
    "coding-workflows": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/coding-workflows",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "coding-workflows"
      ]
    }
  }
}

.mcp.json

{
  "mcpServers": {
    "grounded-cv": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/grounded-cv",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "grounded-cv"
      ]
    },
    "repo-intel": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/repo-intel",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "repo-intel"
      ]
    },
    "web-research": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/web-research",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "web-research"
      ]
    },
    "ats-jobs": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/ats-jobs",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "ats-jobs"
      ]
    },
    "outcome-ledger": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/outcome-ledger",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "outcome-ledger"
      ]
    },
    "coding-workflows": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/coding-workflows",
        "--with",
        "git+https://github.com/prathamesh-git9/mcp-servers.git#subdirectory=packages/common",
        "coding-workflows"
      ]
    }
  }
}

Architecture

flowchart LR
    C[Claude Desktop / MCP client] -->|JSON-RPC over stdio| G[grounded-cv]
    C --> R[repo-intel]
    C --> W[web-research]
    C --> A[ats-jobs]
    C --> O[outcome-ledger]
    C --> CW[coding-workflows]

    G --> H[BM25 + 384-D dense vectors + RRF]
    G --> P[(Committed structured profile)]
    R --> GH[Public GitHub REST API]
    W --> RB[URL policy + robots.txt + extractor]
    A --> ATS[Six public ATS APIs]
    O --> SQ[(SQLite WAL / FULL sync)]
    CW --> RC[Local repo structure + configured quality gates]

    G & R & W & A & O & CW --> CORE["Shared typed failures, deadlines, redaction,<br/>rate limiting and cache"]

The repository is a seven-package workspace: one small shared core and one independently installable package for each server. Every server exposes tools, resources, and at least one prompt over the official MCP Python SDK's stdio transport.

What a hiring manager can verify quickly

Server One-line proof of work
grounded-cv Structured profile resources, genuine hybrid RAG, exact-span citations, and conservative claim verification.
repo-intel Read-only repository, language, topic, README, CI, commit, issue, and code-search intelligence through public GitHub APIs.
web-research Search plus main-content extraction with fail-closed robots checks, SSRF protection, redirect/size limits, and per-host pacing.
ats-jobs One typed model across public Greenhouse, Lever, Ashby, Workable, SmartRecruiters, and Recruitee boards.
outcome-ledger Durable deterministic idempotency keys and an honest outcome_unknown state after a simulated process crash.
coding-workflows Unified-diff review, dependency-aware planning, bounded repo gates, traceback-to-file triage, and conventional commit synthesis.

The complete machine-readable catalog is pinned at docs/manifest.json, and the detailed protocol contract is in docs/API.md.

Grounded CV: demonstrable hybrid RAG

The corpus uses atomic evidence chunking: one independently verifiable fact per chunk, headings retained as metadata, no chunk crossing a section boundary. This makes every returned span independently citable and prevents a partially relevant paragraph from laundering an unsupported claim.

Two independent rankings run for every query:

  1. Okapi BM25 over normalized terms and documented synonym expansion.
  2. Cosine similarity over deterministic 384-dimensional dense feature-hash vectors built from words and character trigrams.

Reciprocal rank fusion combines both lists with RRF(k=60). The verifier then checks claim-token coverage and numeric consistency against the retrieved spans. Supported claims receive exact quotes; unsupported claims receive no citations.

Committed offline fixture, 10 queries, k=5:

Metric Result
recall@5 1.0000
MRR 1.0000
nDCG@5 0.9920

Reproduce it with uv run grounded-cv-eval. The fixture is committed beside the profile, and CI executes the same harness assertions without downloading a model or calling a network service.

Trust and failure contract

  • Every tool has generated input and structured output JSON Schemas backed by Pydantic v2.
  • Every call has both an MCP-level deadline and an upstream HTTP timeout.
  • Public upstream failures become typed values (timeout, blocked, not_found, rate_limited, upstream_error, parse_error, and others); low-level exceptions never cross a tool boundary.
  • GitHub, web, and ATS text is explicitly marked content_is_untrusted; prompts instruct clients not to execute instructions found inside it.
  • Coding diffs, tracebacks, repository text, and gate output are untrusted. Quality gates use shell-free argv execution, credential-reduced environments, and hard deadlines; configured package-manager scripts should run only in repositories the caller trusts.
  • URL credentials, private networks, authenticated scraping, mutation APIs, and secret-shaped ledger fields are rejected. Optional GITHUB_TOKEN improves GitHub rate limits but is never returned or logged.
  • Tests run with outbound sockets disabled except loopback, require no keys, and use protocol clients plus committed provider fixtures.

Development

uv sync --all-packages --dev
uv run ruff check .
uv run ruff format --check .
uv run pytest

CI runs those three gates on Python 3.11, 3.12, and 3.13. Licensed under 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