Which integrations are available for this server?

Supports deployment on Linux systems, enabling Linux users to integrate Pearch.ai's people search capabilities into their applications. Provides installation support for macOS environments, allowing macOS users to deploy the people search functionality on Apple's operating system. Enables integration with Python applications, allowing developers to embed Pearch.ai's powerful people search capabilities within Python-based software.

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., "@Pearch find senior Python developers with AWS experience in San Francisco" 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.

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.

Related MCP server: mcp-spacefrontiers

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

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".