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linkedin-mcp

linkedin-mcp

linkedin-mcp

MCP server for editing your own LinkedIn profile from Claude Code — with a hard propose-then-approve split: the model can draft a change and show you the diff, but only one tool can ever write, and it acts only on a proposal you have seen.

Read this before you install

1. You bring your own LinkedIn app. There is no shared or hosted service here. You create your own app at linkedin.com/developers/apps, and your own client ID and secret go in a file on your own machine (~/.config/linkedin-mcp/.env, permissions 600). The server itself makes no outbound call to anything but LinkedIn's own API — there is no telemetry and no third party. Note that it is still an MCP server: profile data and diffs it returns go to whichever agent you connect, so if that agent is cloud-backed, your profile content reaches that provider like any other chat content. See PRIVACY.md.

2. Writing to your profile needs LinkedIn partner approval — which you must apply for yourself. LinkedIn's Profile Edit API is restricted to LinkedIn-approved partner developers (w_compliance is "a private permission and access is granted to select developers" — see docs/api-notes.md). Approval is granted per developer app, so your app needs your approval; nobody else's carries over. Until it lands, apply_proposal returns an invalid-scope error. That is expected, not a defect — and it may never land, since the permission goes to "select developers".

Everything else works today without partner approval: OAuth sign-in, reading your profile, drafting edits, and reviewing diffs. Only the final write is gated.

Related MCP server: repo-sdk-mcp

How it works

Tool

What it does

Writes to LinkedIn?

auth_start

Prints the LinkedIn OAuth URL, catches the one-shot localhost redirect, exchanges the code, stores tokens

no (OAuth only)

auth_status

Reports whether a token exists and when it expires

no

get_profile

Fetches your profile (GET /v2/me)

no

propose_edit

Builds the exact API request for a change (headline, summary, positions, skills, educations), saves it as a proposal, returns a unified diff + proposal_id

never

list_proposals

Lists saved proposals

no

discard_proposal

Deletes a saved proposal

no

apply_proposal

Sends ONE saved proposal to LinkedIn — the only write tool. Code-enforced confirm gate: it refuses unless called with approval="approve <proposal_id>", a phrase supplied only after the human has reviewed the diff in chat.

yes

Proposals persist under ~/.config/linkedin-mcp/proposals/ so an approval can happen in a later session. Tokens and client credentials live in ~/.config/linkedin-mcp/.env with permissions 600 — entered by you, never by an agent, never committed (see .env.example).

Install

Requires Python ≥ 3.11. Clone the repo, then register it as an MCP server in your agent — add this to your client's MCP config (for Claude Code that is ~/.claude.json, or run claude mcp add linkedin -- bash /path/to/linkedin-mcp/run.sh):

{
  "mcpServers": {
    "linkedin": {
      "command": "bash",
      "args": ["/path/to/linkedin-mcp/run.sh"]
    }
  }
}

Replace /path/to/linkedin-mcp with the absolute path to your clone. run.sh creates .venv/ and installs pinned dependencies on first launch (stamp-gated; all bootstrap output goes to stderr, keeping the MCP stdio channel clean).

Then follow docs/SETUP.md to create your LinkedIn app and sign in — the server has no credentials until you do.

Setup

Follow docs/SETUP.md — it walks through creating your own LinkedIn Developer app, registering the redirect URL, filling ~/.config/linkedin-mcp/.env with your own client ID and secret, running auth_start, and applying for the partner program.

Development

bash run.sh --help                                # bootstraps the venv (runtime deps only)
.venv/bin/pip install -r requirements-dev.txt     # adds pytest + coverage tooling
.venv/bin/python -m pytest                        # offline — every test runs against a mock transport

The test suite includes a granted-write fixture: a mock LinkedIn where every documented write endpoint happily returns 200. Tests assert that propose_edit leaves zero non-GET requests in the recorded log even when writes would succeed, and (positive control) that apply_proposal does record the documented write call in the same fixture. --live-probe is a diagnostic flag (single real request, discriminates "endpoint right but scope not granted" from "endpoint wrong"); it never runs in tests or CI and requires LINKEDIN_MCP_LIVE_PROBE=1.

The API surface is pinned to dated verbatim excerpts from the official docs in docs/api-notes.md.

License

MIT — see LICENSE.

Available Tools

7 tools
apply_proposalA

THE ONLY TOOL THAT WRITES TO LINKEDIN. Sends the single prepared request stored in one proposal. Requires the human to have approved the returned diff in chat first — never call it on your own initiative, and never immediately after propose_edit without that approval. The approval is code-enforced: the 'approval' argument must be exactly 'approve <proposal_id>', supplied only after the human has seen the diff and said yes in chat. Until LinkedIn grants partner access to the Profile Edit API, an invalid-scope / permission error here is EXPECTED and is not a defect: report it plainly and stop, do not attempt any workaround.

Inputs: proposal_id (required), approval. Returns: object

auth_startA

Run the LinkedIn OAuth sign-in. Opens the consent page in a browser, catches the one-shot localhost redirect, exchanges the code, and stores the token in the owner-only config file (0600). Does NOT modify the profile. Returns only non-secret token status — never the token itself. Set url_only=true to just print the authorization URL without starting the listener.

Inputs: url_only, timeout_seconds. Returns: object

auth_statusA

Report whether a LinkedIn access token is stored and when it expires. Reads local config only: no network call, no profile change, and the token value is never returned.

Inputs: no named parameters. Returns: object

discard_proposalA

Delete a saved proposal that the human rejected or that is no longer wanted. Local file removal only — it never touches LinkedIn.

Inputs: proposal_id (required). Returns: object

get_profileA

Fetch the owner's LinkedIn profile with GET /v2/me. Read-only — it cannot change anything. Use it to get the person id and current values before calling propose_edit.

Inputs: no named parameters. Returns: object

list_proposalsA

List saved edit proposals with their diffs. Local read only — no network call and no profile change. Set include_applied=true to also show proposals that have already been sent.

Inputs: include_applied. Returns: object

propose_editA

Draft a profile change WITHOUT sending it. This tool NEVER writes to LinkedIn: it builds the exact API request, saves it as a proposal, and returns a unified diff of the current value versus the proposed one for human review. Show the diff to the human and wait for their approval; only then may apply_proposal be called with the returned proposal_id. section: headline | summary | position | skill | education. changes: for headline/summary use {'text': '...'}; for position/skill/education use {'action': 'create'|'update'|'delete', 'entity_id': '' (for update/delete), 'fields': {...}}. Localized text fields (skill name, position title/companyName/description, education schoolName/degreeName/...) may be given as plain strings — they are wrapped in LinkedIn's documented MultiLocale shape for the chosen locale.

Inputs: changes (required), section (required), locale, person_id. Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

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    TDQS

    A4.6/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool has a distinct, unambiguous purpose: authentication start/status, profile retrieval, proposal creation, listing, discarding, and applying. No two tools overlap in function, and the descriptions clarify the exact role of each.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (e.g., auth_start, get_profile, propose_edit, apply_proposal). The naming is uniform, predictable, and clearly indicates the action and target resource.

    Tool Count5/5

    With 7 tools, the server is well-scoped for its purpose of LinkedIn profile editing. Each tool serves a necessary step in the workflow (auth, read, propose, manage, apply) without redundancy or bloat.

    Completeness5/5

    The tool surface covers the full lifecycle: authentication, profile reading, proposal creation with review, proposal management (list/discard), and the single write operation. The workflow is logically complete, and the explicit guardrails prevent dead ends.

    Maintenance

    ActivityStale
    ResponsivenessNo recent community issues

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