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 | $0.00028 | $0.02180 |
| Opus 5.5 | $0.00011 | $0.00872 |
| Sonnet 5.5 | $0.00006 | $0.00436 |
| Haiku 4.5 | $0.00003 | $0.00218 |
Grade A, and why
2-repro-issue scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 24d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -D /tmp/repro-$NUM-headers -X POST http://127.0.0.1:$PORT/mcp \ Copies of this mod
5 near-identical copies found in the catalogue:
- 2-repro-issue — 100% identical, 156 lines differ
- 2-repro-issue — 100% identical, 156 lines differ
- 2-repro-issue — 100% identical, 156 lines differ
- 2-repro-issue — 100% identical, 156 lines differ
- 2-repro-issue — 100% identical, 156 lines differ
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigate a LinkedIn-MCP issue
Evaluate the reporter packet and the matching source. A live LinkedIn call is optional. Do not check out a PR or attempt a fix. That is /3-verify-pr-fix.
1. Read the issue
NUM=$(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 thread extract the tool, arguments, observed result, runtime, LinkedIn variant, and related issues. Distinguish observations, source findings, hypotheses, and work not run.
Map the tool to code:
linkedin_mcp_server/tools/<surface>.py. MCP entrypoint and arg validationdocs/linkedin-architecture.md. Generated ownership table; follow it tolinkedin_mcp_server/linkedin/<owner>.pyrather than treatinglinkedin/extractor.pyas the implementationlinkedin_mcp_server/linkedin/fields.py.PERSON_SECTIONS/COMPANY_SECTIONS(each entry = one navigation)- The owner-local test, usually
tests/linkedin/test_<owner>.py; usetests/test_fields.py,tests/test_identifiers.py, andtests/test_link_metadata.pyfor those owners, andtests/linkedin/test_facade_*.pyonly for facade contracts
2. Packet and source
Inspect the relevant source at the current SHA. Do not invent a LinkedIn result or an unimplemented tool call.
This step is complete when the report names the issue, the code SHA, the inspected evidence, the facts established, the unverified runtime claims, and the next decision.
Use one of:
- supported by reporter evidence
- confirmed in source
- needs more evidence (list the specific missing fields)
- not supported by the supplied evidence
A successful call on the maintainer's different account never refutes a failure on the reporter's account. Target content language is not the authenticated account's UI language. Captures and URL or attribute evidence may establish the needed variation without another live call.
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.
- 24d ago Changed 3fb603cebc29
- 1mo ago Changed · -16 lines · -47 tokens per session 9c722a3348f2
- 1mo ago Changed 55c82a764d34
- 1mo 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 (4,014 stars, last pushed yesterday), licensed Apache-2.0. It adds 28 tokens to every session and 2,180 once invoked, about $0.0001 per session on Opus 5.5. 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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