📘 This article introduces the Botify MCP, which brings your Botify data into your own LLM. Botify MCP is available with any Botify plan.
Overview
The Model Context Protocol (MCP) is a standard way for an AI assistant to connect to outside data and tools. The Botify MCP lets your AI assistant answer questions using your own SiteCrawler, LogAnalyzer, Google Search Console, and AI Visibility data, along with live search and AI-engine lookups. Instead of pulling reports from separate places, you ask a question in plain language and get an answer built from the data behind it.
How your data stays scoped
Every request you make through the Botify MCP includes your organization and project, and the MCP reaches only the Botify projects your account already has access to. It can query several of those projects in one pass, making portfolio and cross-brand questions possible. Name the projects you want included, or ask which ones were used, because an unqualified request covers everything the account can reach.
How live and stored answers differ
Answers read from live search results, AI Mode, or answer engines query those surfaces in real time, so the same question asked twice can return different results. Answers read from crawl, log, or Search Console data come from a stored snapshot, so the same question returns the same result until the next crawl or log ingestion. Neither is more accurate than the other, but only the stored data from Botify supports comparison over time. Record the date and time alongside any live reading you intend to keep.
What you can ask about
The following highlights questions the Botify MCP can answer, the Botify data behind it, and a use case example.
What you can ask about | What it draws on | Use case example |
Crawl budget and page health | SiteCrawler | |
Pagination depth and where to cut it | SiteCrawler, LogAnalyzer | |
Structured data across a template | SiteCrawler | |
Title, description, and H1 changes before they ship | PageWorkers | |
Search engine crawler and AI crawler activity | LogAnalyzer | |
AI crawler access and brand citations | LogAnalyzer, AI Visibility | |
Search Console performance and ranking decay | Google Search Console | |
Whether a ranking or traffic shift lines up with a change you made | Google Search Console, project annotations | |
What to write next, and whether a page already exists | Keyword data, SiteCrawler | |
What search results and AI answers show right now | Live search and answer engines | |
Your brand's entity and search presence | Live search and answer engines | |
Page performance answers without an export | SiteCrawler, Google Search Console | |
Redirect mapping for a site migration | SiteCrawler | |
Your own business data joined to search data | Editable data catalog | |
Whether an extraction is trustworthy before you build on it | SiteCrawler | |
Recurring reports built from MCP data | Google Search Console, AI Visibility |
How Botify MCP differs from Botify Assist
Botify Assist and Botify MCP both connect you to Botify's data, but they serve different purposes.
Botify Assist is a conversational assistant built into the Botify platform. It uses multiple agents to help you navigate your website data, surface recommendations such as indexation issues or content optimization opportunities, and act on them directly within Botify's interface. Botify Assist works within individual Botify projects.
Botify MCP is an integration layer, not an in-product experience. It exposes Botify's data to external tools and agents to connect Botify to your own agent environment, enabling your team members who don’t use Botify to access your Botify data. Botify MCP can query your Botify data across multiple projects, facilitating reporting across your entire portfolio.
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