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MCP server · AI

Pearch-ai/mcp_pearch

The most accurate people search API/MCP. Natural language in, high-quality candidates out. Built for ATSs and AI Agents.

59Fair

Scored 5 hours ago · breakdown

About Pearch-ai/mcp_pearch

Pearch-ai/mcp_pearch is an MCP server published by Pearch-ai in the AI category: the most accurate people search API/MCP. Natural language in, high-quality candidates out. Built for ATSs and AI Agents.

The repository has 9 stars and 8 forks, with the last commit yesterday. Recent commit activity is the strongest signal that reported issues will get attention.

Install

From source
git clone https://github.com/Pearch-ai/mcp_pearch

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (4)

**get_profile**Look up and enrich a single person by LinkedIn slug or email (contact reveal, real-time refresh, GitHub enrichment).

Parameters not published.

**get_user_info**Authenticated user info: email, remaining credits, pricing plan. Free.

Parameters not published.

**search_company_leads**Find companies and leads/contacts within them (B2B). Example: company *"AI startups in SF, 50–200 employees"* + leads *"CTOs and engineering managers"*, with optional personalized outreach messages.

Parameters not published.

**search_people**Natural-language people search (e.g. *"software engineers in California with 5+ years Python"*). Supports fast/pro/superfast search types, contact reveal & contact filters, real-time profile refresh, insights, and thread-based pagination/follow-ups.

Parameters not published.

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Security checks

Static checks derived from the repository 5 hours ago. Heuristics on the source, not an audit.

  • ✓README presentThe repository ships documentation.
  • ✓License declaredUsable under a stated license.
  • !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
  • !Dependencies pinnedNo lockfile. Installs can pull different code over time.
  • ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

Pearch.ai MCP

Trust Score

MCP server for Pearch.AI: natural-language search over people and companies/leads (B2B). Use it from Claude, Cursor, VS Code, or any MCP-compatible client.

Evaluating AI Recruitment Sourcing Tools by Human Preference

Tools

Tool Description
search_people People search with a natural-language query or structured search_requirements. Supports fast/pro/superfast search types, contact reveal & contact filters, real-time profile refresh, insights, and thread-based pagination/follow-ups.
search_company_leads Find companies and leads/contacts within them (B2B). Example: company "AI startups in SF, 50–200 employees" + leads "CTOs and engineering managers", with optional personalized outreach messages.
get_profile Look up and enrich a single person by LinkedIn slug or email (contact reveal, real-time refresh, GitHub enrichment).
get_user_info Authenticated user info: email, remaining credits, pricing plan. Free.

Structured people search

Pass search_requirements to search_people when you already have a list of criteria:

{
  "search_requirements": [
    {
      "search_requirement": "Software engineer with 5+ years of Python experience",
      "must_have": true
    },
    {
      "search_requirement": "Experience at a fintech company",
      "must_have": false
    }
  ],
  "search_type": "fast",
  "limit": 10,
  "insights": true,
  "reveal_emails": true
}
  • search_requirement: required, non-empty text. Keep each criterion short and self-contained.
  • must_have: true for mandatory criteria, false for nice-to-have preferences (the default).

The list must contain at least one requirement. query is optional when using search_requirements; if both are supplied, the API gives search_requirements precedence. See the Pearch search API contract.

Both inputs support the same search options: search type, insights and scoring, profile refresh, contact reveal and filters, strict filtering, low-confidence results, time budget, pagination, response fields, profile exclusions, and free masked results. Search-type restrictions are the same for both inputs; for example, superfast does not support insights, profile scoring, or high freshness.

For pagination, send the returned thread_id with limit and optionally offset, omitting query and search_requirements. To change criteria, start a new search without thread_id. Existing query calls and thread follow-ups are still forwarded as before; the API decides which follow-up parameters it accepts.

custom_filters is not exposed by this MCP server. Express criteria in query or search_requirements instead.

Remote server (recommended)

The hosted server lives at https://mcp.pearch.ai/mcp and supports two auth schemes at once:

OAuth (no key handling)

Add the server without any headers — OAuth-capable clients (Claude, Cursor, VS Code) discover the flow automatically and open a Google sign-in. Sign in with the Google account whose email matches your Pearch account:

claude mcp add --transport http pearch https://mcp.pearch.ai/mcp

Your searches bill credits against your own Pearch account. If your Pearch account has no API key yet, create one first in the Pearch.ai Dashboard.

API key

Pass the same Pearch API key as api.pearch.ai:

{
  "mcpServers": {
    "Pearch.ai": {
      "url": "https://mcp.pearch.ai/mcp",
      "headers": {
        "Authorization": "Bearer ${env:PEARCH_API_KEY}"
      }
    }
  }
}

Use test_mcp_key for masked (sample) results — no sign-up required.

Local (stdio) installation

Requires Python 3.10+ and pip install -r requirements.txt (or pip install fastmcp).

Claude Desktop / Claude Code

fastmcp install claude-desktop pearch_mcp.py --env PEARCH_API_KEY=test_mcp_key

Or manually under mcpServers:

"Pearch.ai": {
  "command": "python",
  "args": ["/path/to/mcp_pearch/pearch_mcp.py"],
  "env": { "PEARCH_API_KEY": "test_mcp_key" }
}

Cursor

fastmcp install cursor pearch_mcp.py --env PEARCH_API_KEY=test_mcp_key

Replace test_mcp_key with your dashboard key for full results.

Running your own HTTP server

uvicorn pearch_mcp:app --host 0.0.0.0 --port 8000

Health: GET /health or /healthcheck (reports the auth mode).

Environment variables

Variable Default Purpose
PEARCH_MCP_AUTH api_key oauth | api_key | none. In oauth mode raw API-key bearers are still accepted (dual auth).
PEARCH_MCP_BASE_URL http://localhost:8000 Public URL of this server. OAuth metadata, token audience, and the /auth/callback redirect URI all derive from it.
PEARCH_MCP_GOOGLE_CLIENT_ID / PEARCH_MCP_GOOGLE_CLIENT_SECRET — Google OAuth 2.0 Web application client (register <base_url>/auth/callback as a redirect URI). Required in oauth mode.
PEARCH_MCP_INTERNAL_TOKEN — Shared secret for the Pearch API's internal email→API-key mapping endpoint. Required in oauth mode.
PEARCH_MCP_OAUTH_STORE disk redis keeps OAuth state (client registrations, tokens — encrypted) in Redis so it survives restarts and allows >1 replica.
PEARCH_MCP_OAUTH_REDIS_HOST / REDIS_ENDPOINT, REDIS_PORT, PEARCH_MCP_OAUTH_REDIS_DB, REDIS_PASSWORD —, 6379, 4, — Redis connection for the OAuth store.
PEARCH_MCP_ALLOWED_CLIENT_REDIRECT_URIS built-in list Comma-separated fnmatch patterns for MCP client redirect URIs (* = any).
PEARCH_MCP_TOKEN_CACHE_TTL_S 300 Verified-identity cache TTL; also the revocation lag.
PEARCH_API_URL https://api.pearch.ai Pearch API base (per-call base_url overrides).
PEARCH_API_KEY test_mcp_key Fallback key for stdio/local use.
MCP_DISABLE_AUTH — 1 is equivalent to PEARCH_MCP_AUTH=none (local dev only).

In oauth mode the ingress/reverse proxy must route the whole host to the server, not just /mcp: the flow adds /.well-known/oauth-*, /authorize, /token, /register, /revoke, /consent, and /auth/callback.

Development

export PEARCH_API_KEY='test_mcp_key'   # or your key for full results
fastmcp dev inspector pearch_mcp.py

Privacy Policy

Pearch.AI's privacy policy: https://pearch.ai/privacy-policy — covers data collection, usage, storage, third-party sharing, retention, and contact information. Data deletion / opt-out: https://pearch.ai/opt-out. Terms of service: https://pearch.ai/tos.

Support

License

MIT — see LICENSE.

README fetched from the repository 5 hours ago. The original is authoritative.

Questions

About Pearch-ai/mcp_pearch

How do I install Pearch-ai/mcp_pearch?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Pearch-ai/mcp_pearch safe to use with an AI agent?

Its trust score is 59 out of 100 (fair). It passes 3 of the 6 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Pearch-ai/mcp_pearch still maintained?

Yes — the last commit was yesterday. The repository has 9 stars and 8 forks and 1 open issues.