Which integrations are available for this server?

Provides repository intelligence through public GitHub APIs, including read-only access to repository info, language, topics, README, CI, commits, issues, and code-search. One typed model across public greenhouse job boards as part of the ats-jobs integration, enabling interaction with ATS job posting APIs.

How do I use mcp-servers (Six production-minded MCP servers)?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-servers (Six production-minded MCP servers) Evaluate this candidate's CV against the Senior ML Engineer job posting" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

mcp-servers (Six production-minded MCP servers)

by prathamesh-git9

Overview Schema Related Servers Score Discussions

Python

Local

Six production-minded MCP servers

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.

Related MCP server: open-computer-use

`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:

Okapi BM25 over normalized terms and documented synonym expansion.
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.

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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