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pearch-ai/mcp_pearch

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本页由 AI 翻译，技术细节请以原文为准。 查看原文

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Repository Install

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

概览

MCP server for Pearch.AI: natural-language search over and (B2B). Use it from Claude, Cursor, VS Code, or any MCP-compatible client. Evaluating AI Recruitment Sourcing Tools by Human Preference Pass search_requirements to search_people when you already have a list of criteria: - search_requirement: required, non-empty text. Keep each criterion short and self-contained. - must_have: true for mandatory criteria, false for nice-to-have preferences (the default). The list must contain at least one requirement. query is optional when using search_requirements; if both are supplied, the API gives search_requirements precedence. See the Pearch search API contract. Both inputs support the same search options: search type, insights and scoring, profile refresh, contact reveal and filters, strict filtering, low-confidence results, time budget, pagination, response fields, profile exclusions, and free masked results.

README

以下为原文 README 节选，完整内容请查看原文。

Pearch.ai MCP

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

Evaluating AI Recruitment Sourcing Tools by Human Preference

Tools

Tool Description
search_people People search with a natural-language query or structured search_requirements. Supports fast/pro/superfast search types, contact reveal & contact filters, real-time profile refresh, insights, and thread-based pagination/follow-ups.
search_company_leads Find companies and leads/contacts within them (B2B). Example: company “AI startups in SF, 50–200 employees” + leads “CTOs and engineering managers”, with optional personalized outreach messages.
get_profile Look up and enrich a single person by LinkedIn slug or email (contact reveal, real-time refresh, GitHub enrichment).
get_user_info Authenticated user info: email, remaining credits, pricing plan. Free.

Pass search_requirements to search_people when you already have a list of criteria:

{
  "search_requirements": [
    {
      "search_requirement": "Software engineer with 5+ years of Python experience",
      "must_have": true
    },
    {
      "search_requirement": "Experience at a fintech company",
      "must_have": false
    }
  ],
  "search_type": "fast",
  "limit": 10,
  "insights": true,
  "reveal_emails": true
}
  • search_requirement: required, non-empty text. Keep each criterion short and self-contained.
  • must_have: true for mandatory criteria, false for nice-to-have preferences (the default).

The list must contain at least one requirement. query is optional when using search_requirements; if both are supplied, the API gives search_requirements precedence. See the Pearch search API contract.

Both inputs support the same search options: search type, insights and scoring, profile refresh, contact reveal and filters, strict filtering, low-confidence results, time budget, pagination, response fields, profile exclusions, and free masked results. Search-type restrictions are the same for both inputs; for example, superfast does not support insights, profile scoring, or high freshness.

For pagination, send the returned thread_id with limit and optionally offset, omitting query and search_requirements. To change criteria, start a new search without thread_id. Existing query calls and thread follow-ups are still forwarded as before; the API decides which follow-up parameters it accepts.

custom_filters is not exposed by this MCP server. Express criteria in query or search_requirements instead.

在 GitHub 查看完整 README

安装

This server does not publish a one-line install command.

Open the repository installation guide 新手？先看 mcpskill.org 教程 →

配置

{ "mcpServers": { "Pearch.ai": { "url": "https://mcp.pearch.ai/mcp", "headers": { "Authorization": "Bearer ${env:PEARCH_API_KEY}" } } } }