LinkedIn analytics for AI agents
Give your assistant real numbers — impressions, clicks, engagement, per post and per account — and ask it the questions you would never open a spreadsheet for.
Start freeThe analytics you already have, minus the export
Almost nobody reads their LinkedIn analytics regularly, and it is not because the numbers are uninteresting. It is that the question you actually have — did the posts where I was specific do better than the ones where I was vague? — takes twenty minutes of exporting and sorting to answer, and you have other work.
An assistant with read access closes that gap. The question takes one sentence, the answer takes seconds, and because it can also see the post text, it can group and compare on things a dashboard has no column for.
What the agent can read
linkedin_get_post_analytics Impressions, clicks and engagement per post, filterable by date range and by account. This is the one an assistant reaches for when you ask what worked.
linkedin_get_account_summary The same metrics aggregated per account over a range, for when the question is about a month rather than a post.
linkedin_list_posts The post list itself — drafts, scheduled, published, failed — with status, date and origin filters, cursor-paginated so a long history does not overflow the conversation.
linkedin_get_post One post by id, with its schedule and publishing state. Useful when the agent needs to check a specific thing rather than scan.
linkedin_refresh_analytics Pulls fresh numbers from LinkedIn, stalest posts first, and reports how many are left so the agent knows to call again.
linkedin_refresh_post_analytics Refreshes a named list of posts instead of letting the server choose — for a post that just went live, or the handful in one campaign. Returns a result per post, so a failure is traceable to the id that caused it.
Every one of these resolves data for the authenticated user and the
identities they have been granted access to. An agent cannot read another
account, and a client granted only
linkedin.analytics.read
cannot see the content tools at all.
Questions worth asking
- “Which of my posts in the last 90 days got the most impressions, and what did the top three have in common?”
- “Compare engagement on my posts about hiring versus the ones about product. Am I imagining that hiring does better?”
- “Refresh analytics on everything I published this month, then show me the trend week over week.”
- “Which day and time have my best-performing posts actually gone out? Not best practice — mine.”
- “Did the posts I wrote myself outperform the ones I drafted with an agent?”
The last one is the one people find uncomfortable, which is usually a sign it is worth running.
Official API, not scraping
This matters more than it sounds. Tools that scrape LinkedIn or drive the website in a headless browser put the account doing the scraping at risk of restriction, and they break every time LinkedIn changes its markup. Blabigo reads through your own connected account and the permissions you granted it, which is why the integration can be described precisely instead of hedged.
Frequently asked questions
- Can an AI assistant read my LinkedIn analytics?
- Yes. Through Blabigo's MCP server an assistant gets read access to impressions, clicks and engagement for your posts and accounts, plus the post list itself. It reads through your own connected account and the official LinkedIn API — there is no scraping involved.
- Is analytics access read-only?
- The analytics reads are, and they can be granted on their own. A client authorized with the linkedin.analytics.read scope cannot see the content tools at all, so it cannot create, edit, queue or cancel anything. The two refresh tools are the exception in that they cause a fetch from LinkedIn, but they only update stored metrics — they never touch your posts.
- Why does analytics need refreshing at all?
- LinkedIn's numbers keep moving after a post goes out, and pulling live metrics on every question would be slow and would burn API quota for no benefit. Blabigo stores what it has fetched and refreshes on demand, so an agent asking about last quarter answers instantly, while an agent asking about this morning's post can pull fresh figures first.
- Does LinkedIn offer an analytics API for this?
- LinkedIn has an official API, but it is gated behind partner access and returns data for accounts your application is authorized for. That is what Blabigo is doing on your behalf as a connected app. What LinkedIn does not offer is an MCP server, which is why an integration layer is needed for an assistant to use it conversationally.
- What time range can I ask about?
- Whatever Blabigo holds for your connected accounts, which is everything since you connected them. The analytics tools take explicit date filters, so an assistant can scope a question to a week, a quarter or a campaign without pulling your whole history into the conversation.
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