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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add stickerdaniel/linkedin-mcp-server --skill linkedin-mcpgit 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/linkedin-mcp)<a href="https://agentmods.dev/skills/stickerdaniel/linkedin-mcp-server/linkedin-mcp"><img src="https://agentmods.dev/badge/skills/stickerdaniel/linkedin-mcp-server/linkedin-mcp/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/linkedin-mcp"><img src="https://agentmods.dev/badge/skills/stickerdaniel/linkedin-mcp-server/linkedin-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.00575 |
| Opus 5.5 | $0.00017 | $0.00230 |
| Sonnet 5.5 | $0.00009 | $0.00115 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
linkedin-mcp scanned grade A with 0 findings 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 12d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn MCP
Use the bundled linkedin MCP server only when the user explicitly asks for
LinkedIn data or an action on LinkedIn. Do not call LinkedIn tools for unrelated
work, health checks, or background maintenance.
Operating rules
- Start with the smallest read operation that can answer the request.
- Treat profile, company, job, post, feed, and message results as live LinkedIn evidence. Distinguish retrieved facts from inference.
- Keep searches and result pages modest. Do not collect data in bulk or spam.
- Never enable the plugin or its MCP server, edit the host's plugin configuration, or start a login flow merely because LinkedIn might be useful. If either component is disabled, explain that state and stop.
send_messageandconnect_with_personare write actions. Use them only when the user explicitly authorizes the exact recipient and action. Confirm the final message or connection note unless the user has already supplied it.- Do not retry a failed write action unless the result proves it was not sent.
Browser and authentication
The server uses a managed Chromium browser and the user's own LinkedIn session. Installing or enabling the plugin does not sign in. The first LinkedIn data request may prepare the browser, import an existing local session, or require a visible login window. If LinkedIn presents a captcha, two-factor prompt, or other user-owned authentication step, stop and ask the user to complete it.
If browser setup or authentication is still in progress, report that exact state and retry once after it finishes. Do not loop, launch extra browser instances, clear profiles, or replace the user's browser session.
Tool selection
- Profiles:
get_my_profile,get_person_profile,search_people, andget_sidebar_profiles. - Companies:
get_company_profile,get_company_posts,search_companies, andget_company_employees. - Jobs:
search_jobs,get_saved_jobs,get_job_details, andget_job_apply_url. - Content:
get_feedandsearch_posts. - Messages:
get_inbox,get_conversation, andsearch_conversations. - Writes:
send_messageandconnect_with_person, subject to the explicit authorization rules above. - Cleanup: use
close_sessiononly when the user asks to end the managed browser session or when the current LinkedIn task is finished and no follow-up call is expected.
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.
- 12d ago Changed 2395b61b6d78
- 21d ago Changed · +1 lines 95bff5ef64fc
- 25d ago Changed 15fdb28c0df4
- 1mo ago First seen · 52 lines · 43 tokens per session scan A e6f966f53bc0
linkedin-mcp is a skill published in the GitHub repository stickerdaniel/linkedin-mcp-server (4,014 stars, last pushed yesterday), licensed Apache-2.0. It adds 43 tokens to every session and 575 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-08.
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x-use-engage
Research X posts or profiles and prepare persona-voice public replies or individual profile outreach drafts with x-use. Use for public engagement, personalized outreach research, or local lead and campaign preparation; use x-use-inbox for incoming messages, conversation replies, and notifications.
x-use-inbox
Read X direct messages, triage inbox and request folders, send contextual reviewed messages, and inspect notifications with x-use. Use for unread chats, conversation search or replies, individual DMs, and All or Mentions notifications; use x-use-engage for public-post engagement and profile outreach research.
x-use-threads
Read visible X post/reply threads and prepare or resume multi-post drafts with x-use. Use when the user asks to read, compose, review, or continue a public thread on X; use x-use-inbox for direct-message conversations.
x-use-content
Create original X content in the user's persona by researching what works in the niche, drafting posts, and staging them one by one for review. Use when the user wants post ideas, content creation, or a posting pipeline.
x-use-review
Daily digest for an x-use account covering health, metrics, pending drafts, and queued work in one pass, then approve/reject/process per the user's direction. Use for "how is my account doing", daily check-ins, or before any batch of approvals.