Pearch
Best people search engine that reduces the time spent on talent discovery.
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 fastmcporuv 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
mcpServersblock to.vscode/mcp.jsonin your workspace. - Other MCP clients: use the same
command/args/envformat 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
| Tool | Description |
|---|---|
| search_people | Natural-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_leads | Find 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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