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Pearch-ai/mcp_pearch

by Various

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

P

MCP

Pearch-ai/mcp_pearch

Added 1 June 2026

#ai #api #human-resources #mcp

Overview

Pearch-ai/mcp_pearch is a people search API and MCP server that accepts natural language queries and returns high-quality candidate profiles. It is designed for integration with Applicant Tracking Systems (ATS) and AI agents to automate candidate sourcing.

Best for

Best for
Developers building AI agents or ATS integrations that need automated people search

Use cases

  • Search for candidates using natural language job descriptions
  • Integrate with an ATS to enrich candidate pipelines
  • Power an AI agent to find and rank potential hires

How to use

Install

pip install --force-reinstall fastmcp

Tools exposed

  • fastmcp

Tested with

Claude Desktop, Cursor, VS Code

Example client config

[object Object]

Notes

Pearch-ai/mcp_pearch is a people search API and MCP server that accepts natural language queries and returns high-quality candidate profiles. It is designed for integration with Applicant Tracking Systems (ATS) and AI agents to automate candidate sourcing.

7 stars on GitHub. Last updated 2026-05-18. Licensed MIT.

Use cases

  • Search for candidates using natural language job descriptions
  • Integrate with an ATS to enrich candidate pipelines
  • Power an AI agent to find and rank potential hires

Pros

  • Natural language input reduces query complexity
  • Claims high accuracy in candidate matching
  • Built specifically for ATS and AI agent workflows

Cons

  • Low GitHub star count (7) suggests early-stage or limited adoption
  • Requires MCP-compatible environment or API integration
  • No evidence of support for non-people-search use cases

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Natural language input reduces query complexity
  • Claims high accuracy in candidate matching
  • Built specifically for ATS and AI agent workflows

Cons

  • Low GitHub star count (7) suggests early-stage or limited adoption
  • Requires MCP-compatible environment or API integration
  • No evidence of support for non-people-search use cases
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