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The most accurate people search API/MCP. Natural language in, high-quality candidates out. Built for ATSs and AI Agents.
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
test_mcp_key (masked/sample results); set your own key for full results.curl -LsSf https://astral.sh/uv/install.sh | sh) or pippip install fastmcp or uv add fastmcpUse 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).
Clone the repo, then follow the steps for your client:
git clone https://github.com/Pearch-ai/mcp_pearch
cd mcp_pearch
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.
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.
mcpServers block to .vscode/mcp.json in your workspace.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.
| 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).
The server exposes Streamable HTTP at /mcp when run with Uvicorn:
export PEARCH_API_URL='https://api.pearch.ai' # optional
uvicorn pearch_mcp:app --host 0.0.0.0 --port 8000
Health: GET /health or /healthcheck.
Remote access uses the same Pearch API key as api.pearch.ai (Authorization: Bearer). The server validates keys via GET /v1/user. Demo key test_mcp_key is also accepted (masked results).
Cursor ~/.cursor/mcp.json:
{
"mcpServers": {
"Pearch.ai": {
"url": "https://mcp.pearch.ai/mcp",
"headers": {
"Authorization": "Bearer ${env:PEARCH_API_KEY}"
}
}
}
}
export PEARCH_API_KEY='test_mcp_key' # or your key for full results
fastmcp dev inspector pearch_mcp.py
MIT — see LICENSE.
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