English | 简体中文

Octopus LinkedIn

Governed LinkedIn marketing over MCP. Draft, review, publish, comment, and read engagement on LinkedIn — from Claude Desktop, Claude Code, or any MCP-compatible agent — using the official LinkedIn API.

Most "LinkedIn AI" tooling stops at writing the post. The obvious next step is publishing it — and that's where you want governance, not a black box. Octopus LinkedIn makes the whole loop explicit:

draft → review → approve → publish → comment → analyze

Drafting and approving are local-only — they never touch the network. publish_draft is the single gate that sends anything out, and it refuses to publish a draft that hasn't been explicitly approved.

Tools

Content intelligence (LLM-backed)

Conditioned on your brand voice; everything stays behind the approval gate.

Plus MCP prompts (draft_post, repurpose_article, reply_to_comments) and resources (voice://profile, drafts://list) so MCP clients get task templates and live context, not just raw tool calls.

Scope note: the official API only lets you comment on content you control (your own posts, or an org Page you admin). It cannot auto-comment on arbitrary third-party posts — by design. See docs/ARCHITECTURE.md.

LLM configuration

Set one provider and its key (see .env.example):

LLM_PROVIDER=anthropic       # anthropic | openai | gemini
ANTHROPIC_API_KEY=sk-ant-... # or OPENAI_API_KEY / GEMINI_API_KEY
# LLM_MODEL=claude-sonnet-4-6  # optional override

Quick start

1. Install

pip install octopus-linkedin

This installs two commands: octopus-linkedin (CLI) and octopus-linkedin-mcp (the MCP server). To work on the project from source instead, see Development.

2. Create a LinkedIn app

At linkedin.com/developers, create a Standalone app tied to a Company Page, then add these products:

• Share on LinkedIn → grants w_member_social (posting)
• Sign In with LinkedIn using OpenID Connect → grants openid profile email

In the app's Auth tab, add an authorized redirect URL:

http://localhost:8000/callback

3. Configure

cp .env.example .env

Edit .env and paste your Client ID and Client Secret (Auth tab).

4. Authorize (one time)

python -m linkedin.auth

This opens your browser; log in and approve. A token is cached to token.json (gitignored, 0600). Member tokens last ~60 days; re-run this when it expires.

5. Run

python server.py

Connect to Claude Code

claude mcp add octopus-linkedin -- octopus-linkedin-mcp

Or add it to your MCP client config:

{
  "mcpServers": {
    "octopus-linkedin": {
      "command": "octopus-linkedin-mcp"
    }
  }
}

Then just ask: "Draft a LinkedIn post about X, let me review it, then publish."

Example workflow

1. create_draft — "Save this post about our launch."
2. list_drafts / get_draft — review the wording.
3. approve_draft — sign off.
4. publish_draft — it goes live (and only now).
5. get_post_stats — check likes and comments later.

CLI

The same engine ships as a CLI for scripting and cron:

octopus-linkedin authorize
octopus-linkedin post "Hello, world" --visibility PUBLIC
octopus-linkedin draft "A post to review later"
octopus-linkedin drafts --status approved
octopus-linkedin approve drft_abc123 --note "lgtm"
octopus-linkedin schedule drft_abc123 2026-07-02T09:00:00Z
octopus-linkedin run-scheduler --interval 60     # loop: publish due drafts
octopus-linkedin stats urn:li:share:123

Scheduling

Scheduling is split so nothing publishes by surprise: you schedule_draft an approved draft for a future UTC time, then a runner actually sends it when due. Run the runner one of three ways:

• octopus-linkedin run-scheduler — a simple foreground loop, or
• octopus-linkedin publish-due from cron every few minutes, or
• the publish_due MCP tool on demand.

Only drafts that are both approved and past their time are published.

Development

pip install -e ".[dev]"
ruff check . && ruff format --check . && pytest

See CONTRIBUTING.md.

Roadmap

Shipped:

• Scheduled publishing (publish an approved draft at a future time)
• Multi-image posts (up to 9)
• A standalone CLI alongside the MCP server
• Bilingual docs (English | 简体中文)

Content-intelligence layer (shipped):

• LLM backend — Anthropic / OpenAI / Gemini (write / polish / optimize)
• MCP prompts + resources surface
• Draft-from-URL / article repurposing (SSRF-guarded)
• Brand-voice memory (conditions every generation)
• Comment triage on your own posts (classify → draft reply → approve)
• A/B variant generation

Gated (need LinkedIn approval), tracked but not built:

• Company Page posting & engagement (Community Management API)
• Impressions / reach via memberCreatorPostAnalytics (partner-gated, 2025)
• PDF/document posts (need the versioned /rest/posts + Documents API)

Contributions to any of these are welcome.

Security

.env and token.json hold credentials and are gitignored — never commit them. See SECURITY.md for reporting and credential handling.

License

Apache-2.0.