linkedin-discovery-mcp

linkedin-discovery-mcp

Enables discovering public LinkedIn profiles via natural-language criteria, using DuckDuckGo search snippets to return a ranked shortlist with match reasoning, without logging into LinkedIn.

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linkedin-discovery-mcp

An MCP (Model Context Protocol) server that discovers public LinkedIn profiles matching natural-language criteria — e.g. "senior backend engineers in Berlin who know Kotlin, not interns" — and returns a ranked shortlist with match reasoning.

It works entirely through public search snippets (via DuckDuckGo). It never logs into LinkedIn, never automates a LinkedIn session, and never touches any LinkedIn account — so there's no LinkedIn ban risk. The trade-off is shallow data (name, best-effort headline/company/location, profile URL — no full work history, no contact info) and dependence on an unofficial, unauthenticated search backend that can rate-limit or block bursty traffic.

See SETUP.md for install steps, MCP client configuration (Claude Desktop, Claude Code, Antigravity), and known limitations — including a real rate-limit caveat worth reading before assuming this scales to high daily volume.

How it works

  1. An MCP client (Claude, etc.) parses your prompt into structured criteria (role_keywords, skills, location, company, seniority, exclude_terms, ...) and calls the search_linkedin_profiles tool.
  2. src/searchProfiles.ts builds a site:linkedin.com/in query and fetches paginated results from DuckDuckGo (src/duckduckgo.ts), with a polite delay between pages and local caching to avoid redundant fetches.
  3. src/parse.ts filters out non-profile URLs, parses name/headline/company/location from the snippet text, scores each candidate against your criteria, and ranks them.
  4. Results are returned as structured JSON, along with a self-imposed daily request-budget status (get_search_quota_status) so usage stays predictable.

Quick start

npm install
npm run build

Then follow SETUP.md to connect it to your MCP client.

Project layout

src/
  config.ts          # env-driven config (request pacing, cache, budget)
  duckduckgo.ts       # DuckDuckGo HTML search fetch + parse
  quota.ts            # self-imposed daily request budget, persisted to disk
  cache.ts            # local result cache (avoids re-fetching identical searches)
  parse.ts            # snippet parsing + relevance scoring
  searchProfiles.ts    # orchestrates search -> filter -> rank
  types.ts            # shared types
  index.ts            # MCP server entry point (stdio transport, tool definitions)

Limitations (read before relying on this)

  • Discovery only, not enrichment: no emails, phone numbers, or full profiles.
  • DuckDuckGo's LinkedIn index is smaller and staler than Google's.
  • DuckDuckGo will rate-limit/block bursty traffic — see the rate-limit section in SETUP.md for what was actually observed during testing and how to work around it.
  • Currently stdio-only, so it plugs into Claude Desktop, Claude Code, and Antigravity, but not ChatGPT or any client that requires a remote HTTP MCP server.

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