RRepoGEO

REPOGEO REPORT · LITE

stickerdaniel/linkedin-mcp-server

Default branch main · commit d6c6b92a · scanned 6/27/2026, 10:07:16 PM

GitHub: 2,531 stars · 447 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface stickerdaniel/linkedin-mcp-server, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify the unique purpose of the MCP server for AI agents in the README's opening

    Why:

    CURRENT
    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.
    COPY-PASTE FIX
    This is an **MCP (Model Context Protocol) server** specifically designed to empower **AI assistants and Large Language Models (LLMs)** like Claude to programmatically access LinkedIn data. Unlike generic APIs or web scrapers, `linkedin-mcp-server` provides a structured, agent-friendly interface for profiles, companies, jobs, and messages, leveraging your own logged-in browser session.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/stickerdaniel/linkedin-mcp-server
  • mediumtopics#3
    Refine topics to emphasize LLM integration and server role, and remove client-specific ones

    Why:

    CURRENT
    ai-agents, anthropic, chatgpt, chatgpt-desktop, claude, claude-ai, claude-code, claude-desktop, desktop-extension, dxt, linkedin, linkedin-api, linkedin-mcp, linkedin-profile-scraper, linkedin-scraper, mcp, mcp-server, model-context-protocol, python
    COPY-PASTE FIX
    ai-agents, anthropic, claude, linkedin, linkedin-api, linkedin-mcp, linkedin-profile-scraper, linkedin-scraper, mcp, mcp-server, model-context-protocol, python, llm-integration, agent-framework, data-access-layer

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface stickerdaniel/linkedin-mcp-server
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LinkedIn API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LinkedIn API · recommended 1×
  2. Indeed API · recommended 1×
  3. Glassdoor API · recommended 1×
  4. Beautiful Soup · recommended 1×
  5. Scrapy · recommended 1×
  • CATEGORY QUERY
    How can AI agents programmatically access professional network profiles and job listings?
    you: not recommended
    AI recommended (in order):
    1. LinkedIn API
    2. Indeed API
    3. Glassdoor API
    4. Beautiful Soup
    5. Scrapy
    6. Puppeteer
    7. PhantomBuster
    8. Apify

    AI recommended 8 alternatives but never named stickerdaniel/linkedin-mcp-server. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Open-source server to provide large language models with professional social data access?
    you: not recommended
    AI recommended (in order):
    1. Apache Kafka (apache/kafka)
    2. Kafka Connect
    3. ksqlDB (confluentinc/ksqldb)
    4. Supabase (supabase/supabase)
    5. Strapi (strapi/strapi)
    6. Hasura (hasura/graphql-engine)
    7. NocoDB (nocodb/nocodb)
    8. Directus (directus/directus)

    AI recommended 8 alternatives but never named stickerdaniel/linkedin-mcp-server. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of stickerdaniel/linkedin-mcp-server?
    pass
    AI named stickerdaniel/linkedin-mcp-server explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts stickerdaniel/linkedin-mcp-server in production, what risks or prerequisites should they evaluate first?
    pass
    AI named stickerdaniel/linkedin-mcp-server explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo stickerdaniel/linkedin-mcp-server solve, and who is the primary audience?
    pass
    AI named stickerdaniel/linkedin-mcp-server explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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stickerdaniel/linkedin-mcp-server — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite