linkedin-mcp-server is a local MCP server that gives compatible AI assistants access to LinkedIn through a logged-in browser session. Agents can use it to inspect profiles and companies, search jobs, retrieve job details, and manage certain LinkedIn interactions. The catalogue add-ons operate this server and expose its LinkedIn workflows to agents.
Borrowing it
Nothing to install: this file belongs to stickerdaniel/linkedin-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/stickerdaniel/linkedin-mcp-server/main/.agents/skills/2-repro-issue/SKILL.mdgit clone --depth 1 https://github.com/stickerdaniel/linkedin-mcp-serverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/stickerdaniel/linkedin-mcp-server/2-repro-issue)<a href="https://agentmods.dev/skills/stickerdaniel/linkedin-mcp-server/2-repro-issue"><img src="https://agentmods.dev/badge/skills/stickerdaniel/linkedin-mcp-server/2-repro-issue/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/stickerdaniel/linkedin-mcp-server/2-repro-issue"><img src="https://agentmods.dev/badge/skills/stickerdaniel/linkedin-mcp-server/2-repro-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 147 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Data Exfiltration · line 82 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 90 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00075 | $0.02584 |
| Opus 5 | $0.00037 | $0.01292 |
| Sonnet 5 | $0.00015 | $0.00517 |
| Haiku 4.5 | $0.00007 | $0.00258 |
Grade A, and why
2-repro-issue scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -D /tmp/repro-$NUM-headers -X POST http://127.0.0.1:$PORT/mcp \ Copies of this mod
5 near-identical copies found in the catalogue:
- 2-repro-issue — 100% identical, 0 lines differ
- 2-repro-issue — 100% identical, 0 lines differ
- 2-repro-issue — 100% identical, 0 lines differ
- 2-repro-issue — 100% identical, 0 lines differ
- 2-repro-issue — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reproduce a LinkedIn-MCP Issue Locally
Goal: take an issue number, run the exact failing tool call against the real LinkedIn via the local MCP server, and produce concrete evidence — output JSON, error message, partial state — that confirms or refutes the bug on the current branch. Always use the authenticated profile already at ~/.linkedin-mcp/profile/. Never mock.
Phase 1 — Read and map
# Accept "442", "#442", or "https://github.com/.../issues/442" — extract the digits only
NUM=$(echo "$ARGUMENTS" | sed -E 's|.*/||; s|#||g' | grep -oE '^[0-9]+' | head -1)
[ -z "$NUM" ] && { echo "Invalid input: '$ARGUMENTS'. Pass an issue number or URL." >&2; exit 1; }
REPO=stickerdaniel/linkedin-mcp-server
gh issue view $NUM --repo $REPO --comments
From the issue body extract:
- Which MCP tool is affected (
get_person_profile,connect_with_person,search_jobs, …). The issue templates ask for this explicitly. - The exact arguments that trigger the failure (username, company slug, job ID, sections list).
- The expected vs actual behaviour.
- Any locale signal — German UI, non-English profile name, RTL language. Locale-sensitive bugs need a deliberately diverse target.
Map the tool to code so you know where to look if the repro confirms the bug:
linkedin_mcp_server/tools/<surface>.py— MCP entrypoint and arg validationlinkedin_mcp_server/scraping/<feature>.py— actual scraping (extractor.py,connection.py,feed.py,inbox.py, …)linkedin_mcp_server/scraping/fields.py—PERSON_SECTIONS/COMPANY_SECTIONS(each entry = one navigation)- Existing test in
tests/test_scraping.pycovering the same surface
State out loud before running anything: "Reproducing tool X with args Y on branch <current> — expecting <failure mode from issue>."
Phase 2 — Confirm session, branch, dependencies
git status --porcelain | head -5 # workspace must be clean
git log -1 --oneline # record the SHA we're testing
ls ~/.linkedin-mcp/profile/ | head -3 # profile must exist
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 172 lines · 75 tokens per session scan A e85d615973d6
2-repro-issue is a skill published in the GitHub repository stickerdaniel/linkedin-mcp-server (3,384 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 2,584 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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