REPOGEO REPORT · LITE

stickerdaniel/linkedin-mcp-server

Default branch main · commit e6430e21 · scanned 5/17/2026, 12:17:23 AM

GitHub: 1,902 stars · 349 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

28 /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

2 / 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 "MCP" acronym and repo's core purpose in README intro

Why:

CURRENT

Through this LinkedIn MCP server, AI assistants like Claude can connect to your LinkedIn. Access profiles and companies, search for jobs, or get job details.

COPY-PASTE FIX

This LinkedIn Model Context Protocol (MCP) server enables AI assistants like Claude to connect to your LinkedIn. It provides AI agents with programmatic access to profiles, companies, job searches, and messages, allowing them to interact with the professional social network.

mediumhomepage#2
Add repository URL as homepage
Why:
COPY-PASTE FIX
```
https://github.com/stickerdaniel/linkedin-mcp-server
```

mediumtopics#3

Refine topics for better AI agent integration context

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, chatgpt, claude, linkedin, linkedin-api, linkedin-mcp, mcp, mcp-server, model-context-protocol, python, ai-tool-use, agent-tooling, professional-network-api, linkedin-automation, ai-integration

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

Of all named tools, what % are you?

Top rival

Phantombuster

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Phantombuster · recommended 1×
Apify · recommended 1×
Bright Data · recommended 1×
ScrapingBee · recommended 1×
puppeteer/puppeteer · recommended 1×

CATEGORY QUERY
Tool to enable AI agents to interact with professional social network profiles?
you: not recommended
AI recommended (in order):
1. Phantombuster
2. Apify
3. Bright Data
4. ScrapingBee
5. Puppeteer (puppeteer/puppeteer)
6. Playwright (microsoft/playwright)
7. Selenium (seleniumhq/selenium)
AI recommended 7 alternatives but never named stickerdaniel/linkedin-mcp-server. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Python framework to integrate AI models with job search and professional networking platforms?
you: not recommended
AI recommended (in order):
1. Django
2. Flask
3. FastAPI
4. Streamlit
5. Pyramid
6. Tornado
AI recommended 6 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 did not name stickerdaniel/linkedin-mcp-server — likely talking about a different project
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?

Embed your GEO score

Drop this badge into the README of stickerdaniel/linkedin-mcp-server. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/stickerdaniel/linkedin-mcp-server.svg)](https://repogeo.com/en/r/stickerdaniel/linkedin-mcp-server)

HTML

<a href="https://repogeo.com/en/r/stickerdaniel/linkedin-mcp-server"><img src="https://repogeo.com/badge/stickerdaniel/linkedin-mcp-server.svg" alt="RepoGEO" /></a>

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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