Is the Peec AI MCP free?

The MCP server is part of Peec's platform, so you need a Peec plan and you connect it via an authenticated OAuth flow. The protocol itself (MCP) is an open standard with no licence cost — it was donated to the Linux Foundation's Agentic AI Foundation in December 2025.

Can the agent publish content automatically?

It shouldn't. The defensible pattern is a read-only analysis-and-drafting agent with a human ship gate — the agent drafts content, schema and outreach; a person verifies and publishes. Automated publishing, especially off-site community posts, reads as spam and isn't cited.

Do I need to be able to code to build a Peec MCP agent?

You need to be comfortable adding an MCP connector in a host like Claude and reading a tool-call and JSON shape. You don't need to build the server — Peec ships it. Most of the work is the agent's system prompt (the five-stage loop plus the metric semantics) and the human review process.

Which AI engines does the Peec data cover?

Peec tracks brand presence across ChatGPT, Perplexity, Gemini, Google AI Overviews, Google AI Mode, Copilot, Grok and others, addressable in the MCP via stable model_channel IDs — so always break your brand report down by engine rather than relying on the aggregate.

Is my Peec data safe with an agent?

The connection uses OAuth, so the agent never sees your password, and a well-designed agent is read-only by default — it queries reports and never calls the write tools that mutate your project unless a human explicitly approves it.

What's the difference between AEO and GEO?

They're near-synonyms — Answer Engine Optimization and Generative Engine Optimization both mean getting your brand cited in AI-generated answers. A Peec MCP agent serves both, because it works from the same underlying citation data.

AI Search Peec MCP Model Context Protocol AEO GEO AI Agents AI Search Agent Schmitdy Citation Share

How to Build AI Agents on the Peec AI MCP for AEO and GEO (2026)

Marco Lobo

6 June 2026·9 min read

TL;DR

In late 2025 Peec AI shipped an official MCP server, which makes its entire AI-search measurement layer — projects, prompts, brand visibility per engine, full AI answer transcripts, the source/citation graph, and scored Actions — directly callable by an LLM agent.

That means you can wire a Claude or GPT agent straight to the ground truth of how ChatGPT, Perplexity, Gemini and AI Overviews actually answer your category — and have it reason over the data, then draft the work to close the gaps.

The build pattern is a five-stage read-only loop: baseline → find the gap → read the answers → pull the recommendations → draft the work package, with a human ship gate before anything publishes.

The honest part: the agent triages and drafts; a human verifies and ships. Dashboards measure; the agent + human move the number.

Written by Schmitdy — we build production agents on the Peec MCP. Last updated June 2026.

The bottom line

The Peec MCP turns AI-search measurement into something an agent can act on — baseline the category, find the off-domain gaps, read the actual answers, and draft the work to close them, with a human shipping the result. The data is finally agent-callable; the execution is still where the value is. Start with the source gap. Then move the answer.

If you'd rather not build and run the agent yourself, that's exactly what Schmitdy does: an AI-search agent built on this pattern, with humans on quality and outreach.

Frequently Asked Questions

AI Search Peec MCP Model Context Protocol AEO GEO AI Agents AI Search Agent Schmitdy Citation Share

Marco Lobo

Founder, Schmitdy

Marco builds AI search growth systems that turn prompts, sources, content, and agents into pipeline for B2B teams.

LinkedIn X / Twitter

Handdrawn editorial illustration on a cream background split into three panels: on the left, a dashboard with a gauge and bar chart labelled tool; in the middle, three people standing together labelled agency; on the right, a friendly robot wearing a headset talking with a person labelled agent — the three layers of improving AI search visibility

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AI Search Tool vs Agency vs Agent: Which Do You Actually Need in 2026?

There are three ways to improve your AI search visibility, and they're layers of the same stack, not rivals: a tool (Peec, Profound) measures where you stand; an agency executes the optimisation work with people; an agent — an AI-search agent — does the recurring volume under human review. A tool changes your understanding, not the answer. An agency changes the answer but costs the most and moves at human speed. An agent does the volume at mid-market cost with humans owning quality and strategy. In 2026 most teams keep a tool and add one execution layer — the real decision is which one.

Marco Lobo·6 Jun 2026·7 min read

Handdrawn editorial illustration on a cream background: an AI-search dashboard of charts and a magnifying glass on the left feeds through a funnel in the centre, which sorts into three citation chips — a review star, a community comment, and a reference source — that flow into an AI answer speech bubble on the right; the path from dashboard data to AI citations

AI SearchAI SearchPeec

From Peec or Profound Data to AI Citations: A 5-Step Playbook (2026)

Your Peec or Profound dashboard tells you where you're invisible in AI answers, but a visibility score is an outcome, not an instruction. Turning the data into citations is a five-step job: read the gap reports rather than the vanity metric, turn the cited sources into a third-party target list, fix the pages that are retrieved but not cited, build the off-site footprint the data points to, then re-measure the delta on a cadence. The highest-leverage move is the source/domain gap report — the third-party sites citing your competitors but not you. Most of the work lands off your own domain and has to recur, which is the gap between owning a dashboard and moving its numbers.

Marco Lobo·6 Jun 2026·7 min read

Handdrawn editorial illustration on a cream background splitting two worlds: on the left, a stack of classic search result pages with a search bar and ten blue ranked links labelled SEO; on the right, an AI answer speech bubble with a small robot, lines of generated text, and three cited-source chips labelled AEO / GEO, the shift from ranking links to being cited inside the AI answer

AI SearchAEOGEO

AEO vs GEO vs SEO: The Difference, and Which One You Need in 2026

SEO ranks links in classic search. AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) optimise to be cited inside the AI-generated answer in ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot. AEO and GEO are near-synonyms for the same goal; SEO is a separate, still-necessary discipline that feeds the retrieval pool. The practical answer in 2026 is that you do all three, but you measure AEO/GEO on citation share, not blue-link position, and the winning levers (off-site footprint, entity authority, content shape, freshness) sit mostly off your own site.

Marco Lobo·6 Jun 2026·9 min read

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