Pearch

Best people search engine that reduces the time spent on talent discovery.

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Pearch.ai MCP

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

Evaluating AI Recruitment Sourcing Tools by Human Preference

Features

  • search_people — natural-language search for people (e.g. “software engineers in California with 5+ years Python”); returns candidates with optional insights and profile scoring.
  • search_company_leads — find companies and leads/contacts within them (B2B); e.g. “AI startups in SF, 50–200 employees” + “CTOs and engineering managers”.
  • Test key by default — works out of the box with test_mcp_key (masked/sample results); set your own key for full results.

Prerequisites

  • Python 3.10+
  • uv (recommended; Linux/macOS: curl -LsSf https://astral.sh/uv/install.sh | sh) or pip
  • FastMCP — install with pip install fastmcp or uv add fastmcp

API key

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

For full, unmasked results, get an API key from the Pearch.ai Dashboard and set it as PEARCH_API_KEY in your MCP config (see Installation below).

Installation

Clone the repo, then follow the steps for your client:

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

Claude Desktop

Automatic:

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

Replace test_mcp_key with your dashboard key for full results.

If you see bad interpreter: No such file or directory (e.g. with conda), run:

pip install --force-reinstall fastmcp

or:

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

Manual: edit ~/.claude/claude_desktop_config.json and add under mcpServers. Replace /path/to/mcp_pearch with your actual path.

With uv:

"Pearch.ai": {
  "command": "uv",
  "args": ["run", "--with", "fastmcp", "fastmcp", "run", "/path/to/mcp_pearch/pearch_mcp.py"],
  "env": { "PEARCH_API_KEY": "test_mcp_key" }
}

With pip/conda (no uv):

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

Ensure fastmcp is installed: pip install fastmcp.

Cursor

Recommended (automatic):

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

Replace test_mcp_key with your dashboard key for full results.

Manual: add to ~/.cursor/mcp.json (or project .cursor/mcp.json):

{
  "mcpServers": {
    "Pearch.ai": {
      "command": "uv",
      "args": ["run", "--with", "fastmcp", "fastmcp", "run", "/absolute/path/to/pearch_mcp.py"],
      "env": { "PEARCH_API_KEY": "test_mcp_key" }
    }
  }
}

Replace /absolute/path/to/pearch_mcp.py with the real path. Use test_mcp_key for masked results, or your dashboard key for full results.

To generate a ready snippet:

fastmcp install mcp-json pearch_mcp.py --name "Pearch.ai"

Then paste the output into mcpServers in ~/.cursor/mcp.json.

VS Code and other clients

  • VS Code: add the same mcpServers block to .vscode/mcp.json in your workspace.
  • Other MCP clients: use the same command / args / env format in the client’s MCP config.

Generate a config snippet (defaults to test_mcp_key; add --env PEARCH_API_KEY=your-key for full results):

fastmcp install mcp-json pearch_mcp.py --name "Pearch.ai"

Paste the generated object into your client’s mcpServers.

Tools

ToolDescription
search_peopleNatural-language search for people or follow-up on a thread. Example: "software engineers in California with 5+ years Python", "senior ML researchers in Berlin".
search_company_leadsFind companies and leads/contacts (B2B). Example: company "AI startups in SF, 50–200 employees" + leads "CTOs and engineering managers".

Base URL: PEARCH_API_URL or per-call base_url (default https://api.pearch.ai).

Development

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

Support

License

MIT — see LICENSE.

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