Skip to main content
Glama
Sign In
deenesjakoruzh
linkedin-profile-scraper

linkedin-profile-scraper

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

Lakome LinkedIn MCP Server

Smithery

경량 Model Context Protocol(MCP) 서버로, Lakome API를 기반으로 AI 에이전트에게 실시간 LinkedIn 프로필 추출 기능을 제공합니다.

이 서버는 LLM 클라이언트(예: Claude Desktop, Cursor, Cline, Roo Code 및 사용자 지정 AI 에이전트)와 Lakome LinkedIn 인텔리전스 엔드포인트 간의 브리지 역할을 합니다.

⚡ 기능

  • 표준 stdio 전송: 모든 표준 MCP 클라이언트와 호환됩니다.

  • 단일 특화 도구: extract_linkedin_profile을 노출하여 전체 구조화된 프로필 정보(경력, 교육, 기술, 헤드라인, 요약, 연락처 정보 등)를 추출합니다.

  • 일괄 처리: 단일 요청에서 LinkedIn 프로필 URL 하나 또는 여러 개를 조회할 수 있습니다.

  • 오류 처리: 인증 키를 검증하고 형식화된 오류 피드백을 반환합니다.

Related MCP server: SalesTouch

📋 사전 요구사항

  • Node.js v18.0.0 이상

  • Lakome API 키 (mcp.lakome.in에서 받으세요)

🔑 LAKOME_API_KEY 설정

서버는 Lakome API 엔드포인트에 대한 요청을 인증하기 위해 사용자의 LAKOME_API_KEY가 필요합니다.

macOS / Linux의 경우

export LAKOME_API_KEY="your_actual_lakome_api_key_here"

Windows(PowerShell)의 경우

$env:LAKOME_API_KEY="your_actual_lakome_api_key_here"

Windows(명령 프롬프트)의 경우

set LAKOME_API_KEY=your_actual_lakome_api_key_here

🚀 설치 및 클라이언트 구성

1. Claude Desktop 설정

claude_desktop_config.json에 서버 구성을 추가하세요.

  • 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"
      }
    }
  }
}

Windows 사용자 참고 사항: /absolute/path/to/mcp-server/index.js를 사용자의 Windows 절대 경로(예: D:\\extensions and projects\\lakome API project\\mcp-server\\index.js)로 바꾸세요.

2. Cursor IDE 설정

프로젝트의 .cursor/mcp.json 또는 전역 MCP 설정에 다음을 추가하세요:

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

3. Smithery.ai CLI

Smithery를 통해 서버를 자동으로 설치하고 실행할 수 있습니다:

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

🛠️ 도구 참조

extract_linkedin_profile

하나 또는 여러 LinkedIn 프로필 URL에서 포괄적이고 구조화된 프로필 데이터와 정보를 추출합니다.

매개변수

이름

타입

필수 여부

설명

profileUrls

string[]

예

추출할 LinkedIn 프로필 URL 배열 (예: ["https://www.linkedin.com/in/williamhgates"])

호출 예시

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

응답 출력 예시

[
  {
    "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"
  }
]

🧪 로컬 테스트 및 개발

  1. 의존성 설치:

    cd mcp-server
npm install

  2. 서버를 로컬에서 실행:

    LAKOME_API_KEY="your_api_key" node index.js

  3. MCP Inspector로 검사:

    npx @modelcontextprotocol/inspector node index.js

📄 라이선스

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
Let your AI agent see the webWeb-to-screenshot MCP for Claude, Cursor and Codex. No cookie banners. Free to start.snap-render.comAd

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

Related MCP Connectors

Related MCP Servers

  • LinkedIn MCP Pro Max

    mdnaimul22
    A
    license
    A
    quality
    D
    maintenance
    High-performance autonomous MCP server that turns LinkedIn into an API for AI workflows, enabling profile management, job search, content posting, and document generation.
    14
    1
    MIT

  • SalesTouch

    antoineDsh
    A
    license
    Not graded
    quality
    C
    maintenance
    MCP server for AI-native LinkedIn prospecting. It enables lead research, audience building, conversation management, and controlled outreach actions such as messaging and publishing through an OAuth-protected remote endpoint.
    2
    MIT

  • MCP Server for LinkedIn

    bartest5
    A
    license
    A
    quality
    D
    maintenance
    An MCP server that lets AI assistants like Claude read LinkedIn data through your own logged-in browser session. Access profiles and companies, search for jobs, or get job details.
    17
    Apache 2.0