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linkedin-profile-scraper

linkedin-profile-scraper

by lakome-learn-n-grow
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Lakome LinkedIn MCP Server

Smithery

Ein leichtgewichtiger Model Context Protocol (MCP)-Server, der KI-Agenten die Echtzeit-Extraktion von LinkedIn-Profilen ermöglicht, unterstützt durch die Lakome API.

Dieser Server dient als Brücke zwischen LLM-Clients (wie Claude Desktop, Cursor, Cline, Roo und benutzerdefinierten KI-Tools) und dem LinkedIn-Intelligence-Endpunkt von Lakome.

⚡ Funktionen

  • Leichtgewichtiger stdio-Transport: Kompatibel mit jedem standardkonformen MCP-Client.

  • Ein einziges spezialisiertes Tool: Stellt extract_linkedin_profile bereit, um vollständige strukturierte Profildetails zu extrahieren (Berufserfahrung, Ausbildung, Fähigkeiten, Überschrift, Zusammenfassung, Kontaktinformationen, Kontaktinformatien und mehr).

  • Stapelverarbeitung: Unterstützt das Abfragen einer einzelnen oder mehrerer LinkedIn-Profil-URLs in einer einzigen Anfrage.

  • Fehlerbehandlung: Validiert Authentifizierungsschlüssel und gibt aussagekräftige Fehlermeldungen zurück.

Related MCP server: SalesTouch

📋 Voraussetzungen

  • Node.js v18.0.0 oder höher

  • Einen Lakome API Key (Holen Sie sich Ihren unter mcp.lakome.in)

🔑 Einrichten Ihres LAKOME_API_KEY

Der Server benötigt Ihren LAKOME_API_KEY, um sich über den Lakome-API-Endpunkt zu authentifizieren.

Auf macOS / Linux

export LAKOME_API_KEY="your_actual_lakome_api_key_here"

Unter Windows (PowerShell)

$env:LAKOME_API_KEY="your_actual_lakome_api_key_here"

Unter Windows (Eingabeaufforderung)

set LAKOME_API_KEY=your_actual_lakome_api_key_here

🚀 Installation & Client-Konfiguration

1. Claude Desktop einrichten

Fügen Sie die Serverkonfiguration zu Ihrer claude_desktop_config.json hinzu:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "lakome-linkedin": {
      "command": "node",
      "args": [
        "/absolute/path/to/mcp-server/index.js"
      ],
      "env": {
        "LAKOME_API_KEY": "your_actual_lakome_api_key_here"
      }
    }
  }
}

Hinweis für Windows-Benutzer: Ersetzen Sie /absolute/path/to/mcp-server/index.js durch Ihren absoluten Windows-Pfad (z. B. D:\extensions and projects\lakome API project\mcp-server\index.js).

2. Cursor IDE einrichten

Fügen Sie das Folgende zu den MCP-Einstellungen Ihres Projekts (.cursor/mcp.json) oder den globalen MCP-Einstellungen hinzu:

{
  "mcpServers": {
    "lakome-linkedin": {
      "command": "node",
      "args": [
        "./mcp-server/index.js"
      ],
      "env": {
        "LAKOME_API_KEY": "your_actual_lakome_api_key_here"
      }
    }
  }
}

3. Smithery.ai CLI

Sie können den Server automatisch über [Smithery](https://smithery.ai/installieren und ausführen):

npx -y @smithery/cli install @lakome/linkedin-mcp --client claude

🔧 Tool-Referenz

extract_linkedin_profile

Extrahiert umfassende, strukturierte Profil- und Analyse-Daten aus einer oder mehreren LinkedIn-Profil-URLs.

Parameter

Name

Typ

Erforderlich

Beschreibung

profileUrls

string[]

Ja

Array von LinkedIn-Profil-URLs, die extrahiert werden sollen (z. B. ["https://www.linkedin.com/in/williamhgates"])

Beispielaufruf

{
  "profileUrls": [
    "https://www.linkedin.com/in/williamhgates",
    "https://www.linkedin.com/in/satyanadella"
  ]
}

Beispielantwort

[
  {
    "url": "https://www.linkedin.com/in/williamhgates",
    "data": {
      "fullName": "Bill Gates",
      "headline": "Co-chair, Bill & Melinda Gates Foundation",
      "location": "Seattle, Washington, United States",
      "summary": "Co-chair of the Bill & Melinda Gates Foundation. Founder of Breakthrough Energy...",
      "experiences": [
        {
          "title": "Co-chair",
          "company": "Bill & Melinda Gates Foundation",
          "dateRange": "2000 - Present"
        }
      ],
      "education": [
        {
          "schoolName": "Harvard University",
          "degreeName": "Honorary Doctorate",
          "fieldOfStudy": "Law"
        }
      ],
      "skills": ["Philanthropy", "Software Development", "Global Health"]
    },
    "status": "success"
  }
]

🧪 Lokale Tests & Entwicklung

  1. Abhängigkeiten installieren:

    cd mcp-server
npm install

  2. Server lokal ausführen:

    LAKOME_API_KEY="your_api_key" node index.js

  3. Mit dem MCP Inspector prüfen:

    npx @modelcontextprotocol/inspector node index.js

📄 Lizenz

MIT © Lakome

Available Tools

1 tool
extract_linkedin_profileA

Extracts comprehensive, structured profile data and intelligence from one or more LinkedIn profile URLs using the Lakome API.

NameRequiredDescriptionDefault
profileUrlsYesArray of full LinkedIn profile URLs to extract (e.g. ["https://www.linkedin.com/in/williamhgates"])

TDQS

A3.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

When annotations are absent, the description carries the full disclosure burden. It only says 'extracts' and 'using the Lakome API', but leaves out whether network access is required, whether authentication is needed, whether any state changes occur, and what 'intelligence' entails.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence conveys the action, resource, input cardinality, and backend API. There is no fluff, and the key detail that this is a batch-style URL extraction is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The core purpose is clear, but the absence of an output schema and behavioral notes means the agent lacks information about the structure of the returned data, failure behavior, or what data is actually considered 'intelligence'. For such a simple tool, the bare-bones description is minimal but still has noticeable gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents the single parameter (profileUrls) with an example, giving 100% coverage. The description adds no new parameter-level information beyond restating the one-or-more constraint.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('extract') and identifies the resource clearly: comprehensive structured profile data and intelligence from LinkedIn profile URLs via the Lakome API. It distinguishes the tool's scope immediately, including the ability to handle one or more URLs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The usage context is explicit: call this tool when you need structured LinkedIn profile data from the given profile URLs. No exclusions or alternative tools exist in the provided context, so the clear scenario description is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedextract_linkedin_profile
Deploy Server
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TDQS

A3.9/5.0

Scored across 1 tool

Disambiguation5/5

Only one tool exists, so there is zero ambiguity between tools. The tool's purpose is clearly defined and distinct.

Naming Consistency5/5

The single tool follows a clear verb_noun pattern (extract_linkedin_profile), which is consistent and descriptive.

Tool Count3/5

A single tool is borderline for a server, but for a focused LinkedIn profile scraper it is not unreasonable. Still, the surface is minimal and offers no auxiliary capabilities.

Completeness5/5

The tool's description indicates it extracts comprehensive structured profile data from one or more URLs, which fully covers the server's stated purpose. No obvious gaps in the domain of LinkedIn profile scraping.

Maintenance

ActivityStale
ResponsivenessNo recent community issues

Resources

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