AI for LinkedIn
Connect Claude and other AI assistants to your LinkedIn account, let them draft and queue posts, and ask them what actually performed — through an official integration with limits you can check.
No agent can publish instantly. No agent can delete a post.
Connect your assistant
Three setup guides, one endpoint. Pick the one you already use.
Connect to Claude
A two-minute setup walkthrough for Claude Desktop, Claude Code and any config-file client — plus what to do when the connection fails.
Setup guide →
Connect to ChatGPT
Custom connectors, what ChatGPT supports today, and the fallback path when your plan does not offer them.
Setup guide →
Connect to Gemini
One command in Gemini CLI, automatic OAuth discovery, and the settings.json form for anyone who prefers to edit it by hand.
Setup guide →Start here
10 pages covering the whole picture, from the protocol to the prompts.
LinkedIn MCP server
The endpoint, all 13 tools, the three OAuth scopes, and the two ways to authenticate. Start here if you want the technical picture.
Read more →Connect to Claude
A two-minute setup walkthrough for Claude Desktop, Claude Code and any config-file client — plus what to do when the connection fails.
Read more →Scheduling with AI
The draft → review → queue loop, the prompts that make it work, and why an agent that queues beats an agent that publishes.
Read more →Analytics for agents
Read-only access to impressions, clicks and engagement, so you can ask your assistant what actually worked instead of exporting spreadsheets.
Read more →Agent guardrails
The written safety contract: no instant publish, no delete tool, hard server-side quotas, and an audit trail per credential.
Read more →LinkedIn API alternative
Why LinkedIn's own API is hard to get, what you can and cannot do without partner access, and how to ship without waiting on an approval queue.
Read more →MCP vs Zapier & n8n
Where trigger-action automation is the right tool, where a conversational agent is, and why the answer is usually both.
Read more →Connect to ChatGPT
Custom connectors, what ChatGPT supports today, and the fallback path when your plan does not offer them.
Read more →Connect to Gemini
One command in Gemini CLI, automatic OAuth discovery, and the settings.json form for anyone who prefers to edit it by hand.
Read more →Claude Code & CLI
For developers: the MCP transport, the CLI, exit codes, idempotency keys, and shipping release notes from CI.
Read more →Three different things get called “AI for LinkedIn”
They are not competing — most people end up using all three — but they fail in different ways, and it is worth knowing which one you are buying. The shift worth paying attention to is the second one: for the first time, the assistant you already talk to can hold your actual LinkedIn account in context.
Assistive AI: it writes, you publish
The first wave. You open a generator, describe a post, get a draft, edit it, publish it. Useful, and still the fastest way to get unstuck on a blank page — but the AI lives in a separate tab and knows nothing about your account, your past posts, or what worked.
Try the AI post generator →Agentic AI: it works inside your account
The current wave, and what this section is mostly about. Your assistant connects to Blabigo over MCP and gets real tools — it can look at what you posted last month, draft something in the same voice, and put it on your queue for Thursday. The work happens in one conversation instead of six tabs.
See the MCP server →Analytical AI: it tells you what happened
The part people underrate. Once an assistant can read your real impressions and engagement, questions you would never open a spreadsheet for become one sentence: which of my posts about hiring did best, and what did they have in common?
Analytics for agents →The part most tools skip
Giving software write access to the account your professional reputation lives on is a real decision, and “our AI is safe” is not an answer to it. So we wrote the limits down as specifics instead: an agent cannot publish immediately, there is no tool that deletes a post, quotas are enforced on the server rather than requested politely of the model, and every post an agent creates is badged with the credential that made it.
Those limits bind the agent, the API, the CLI and the web app equally, which is the only way a limit means anything.
Read the guardrails in fullAI tools you can use right now
No setup, no connection, no agent required.
AI LinkedIn Post Generator
Generate a full post from a prompt, an idea, or a link — then edit it in the composer.
Open tool →AI Carousel Generator
Turn a topic into a multi-slide LinkedIn carousel without opening a design tool.
Open tool →LinkedIn Headline Generator
Rewrite your profile headline until it says what you actually do.
Open tool →Frequently asked questions
- What is an MCP server, in plain terms?
- The Model Context Protocol is an open standard that lets an AI assistant use an outside service through a defined set of tools. An MCP server publishes those tools; the assistant calls them. Blabigo's MCP server publishes LinkedIn tools, so an assistant like Claude can read your analytics or queue a post without anyone building a custom integration first.
- Can an AI agent post to LinkedIn on my behalf?
- It can write a post and put it on your queue with a scheduled time. It cannot publish one. Blabigo's scheduler does the publishing at the time on the post, which means there is always a window in which you can read what an agent wrote and cancel it. No tool in the integration reaches LinkedIn directly.
- Does this use the official LinkedIn API or scraping?
- The official API, through your own connected account and its normal permissions. Nothing here scrapes LinkedIn, automates the LinkedIn website in a browser, or logs in as you. That distinction matters: scraping tools put your account at risk of restriction, and they break whenever LinkedIn changes its markup.
- Which AI assistants work with this?
- Any client that speaks the Model Context Protocol. That includes Claude Desktop, Claude Code, and other connector-based assistants, plus anything you build yourself with an MCP client library. Clients that cannot reach a remote server can run a local command that speaks MCP over stdio instead.
- Do I need to be able to write code?
- No. In connector-based clients you paste one URL and approve the connection in your browser, the same way you would authorize any other app. Writing code only becomes relevant if you want to drive the integration from your own scripts.
Give your assistant a LinkedIn account
Create a free Blabigo account, connect LinkedIn, and point Claude at it.
Start free