linkedin-mcp-server
Open-source MCP server for LinkedIn. Give Claude and any MCP-compatible AI agent access to profiles, companies, jobs, and messages.
Runs with mockssource: GitHubPythonApache-2.0commit f410bfdc3256
Python, Apache-2.0 licensed. The project labels itself: ai agents, anthropic, chatgpt, chatgpt desktop, claude, claude ai, claude code and claude desktop.
linkedin-mcp-server runs, with stand-ins for the services it depends on. An Argusic agent installed it in 0.7 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Project installs cleanly with uv sync, server imports OK, test suite passes all non-environment-dependent tests (2921 of 3014 selected pass with the fix applied; the 11 failures are pre-existing container/TERM mismatches).
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 92 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- Test test_slow_profile_ownership_cannot_block_the_deadline fails in container environments because it doesn't call initialize_bootstrap('managed') before start_background_browser_setup_if_needed. In a container, runtime policy defaults to DOCKER causing early return without error.20 minutes
- Install time
- under a minute
- Cold machine to finish
- 30 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- Model tokens used
- 128,783
- Exact commit tested
- f410bfdc3256
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- How the result was proved
- runs before 2026-09-07 did not record the command used to check the result
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
fallback = threading.Timer(0.2, release.set)
fallback.start()
started = asyncio.get_running_loop().time()
38: initialize_bootstrap,
106: initialize_bootstrap("managed")
130: initialize_bootstrap("managed")
163: initialize_bootstrap("managed")
172: initialize_bootstrap("managed")
Good, the change is exactly what's needed. Now let me compile my final report.
tokens used
128,783
Good, the change is exactly what's needed. Now let me compile my final report.Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Installed in 0.7 minutes, faster than the median of the 11 comparable projects Argusic has measured.
- Recovered from all 1 error without a human stepping in, which says the failures are documented well enough to solve.
- Licensed Apache-2.0, as reported by its host.
- Measured 3 times, so the result is not a one-off.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
Of the 12 ai-agents projects Argusic has installed and timed, linkedin-mcp-server was the 3rd fastest to reach a running state, and 8 of 12 reached one at all.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.18 | |
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.07 | |
| Argusic Runner | 1/3 | Ran out of time | 0.00 | 0.25 |
Topics (from GitHub)
ai-agentsanthropicchatgptchatgpt-desktopclaudeclaude-aiclaude-codeclaude-desktopdesktop-extensiondxtlinkedinlinkedin-apilinkedin-mcplinkedin-profile-scraperlinkedin-scrapermcpmcp-servermodel-context-protocolpython
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/linkedin-mcp-server)Questions
- Does linkedin-mcp-server run?
- linkedin-mcp-server runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 1 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test linkedin-mcp-server?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit f410bfdc3256. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test linkedin-mcp-server?
- The run that produced this verdict cost $0.18: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does linkedin-mcp-server take to install?
- 0.7 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does linkedin-mcp-server need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing linkedin-mcp-server?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does linkedin-mcp-server compare with the alternatives?
- Of the 12 ai-agents projects Argusic has installed and timed, linkedin-mcp-server was the 3rd fastest to reach a running state, and 8 of 12 reached one at all.
- Where is the evidence for linkedin-mcp-server?
- All 3 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.