Skip to main content
This documentation is built to be read by AI assistants as well as people. You can connect it to your assistant so that answers come from the current FlowX.AI documentation rather than from whatever the model happened to memorize during training.
This page is about connecting the documentation site to an AI assistant. It is not about the MCP Server data source you configure inside a project. For that, see MCP integration overview.

​
Why connect the docs

Language models are trained at a point in time and FlowX.AI ships monthly. An assistant answering from memory will confidently describe features that changed, node types that were renamed, and configuration keys that never existed. Connecting the docs changes where the answer comes from. The assistant searches the live documentation, reads the actual page, and cites it back to you.
This matters most for version-sensitive questions. Availability, environment variables, and node configuration differ between releases, and those are exactly the details a model is most likely to get wrong from memory.

​
Connect from any page

Every page on this site has a contextual menu at the top with the connection options built in.
1

Open the menu on any documentation page

Use the control at the top of the page, beside the page title.
2

Pick your assistant or editor

Options include Claude, ChatGPT, Perplexity, Cursor, and VS Code. Each one hands the current page, or the connection details, straight to that tool.
3

Ask your question

The assistant now has the documentation available and will search it rather than answering from memory.
The same menu also offers Copy page and View as Markdown if you would rather paste content in manually.

​
Connect manually over MCP

For any client that supports the Model Context Protocol, connect to this endpoint over HTTP:
In Claude Code, add it with:
Other clients store MCP server definitions in their own configuration file, and most accept a remote HTTP server as a name and URL pair. Use the contextual menu on any page to get the exact configuration for Cursor and VS Code, since it generates the correct file and format for you.

​
What the assistant can do once connected

The docs server exposes three tools.
query_docs_filesystem_docs runs against a sandboxed, in-memory copy of the documentation. It is not a shell, and nothing runs on your machine, the docs server, or any FlowX environment.
submit_feedback is worth knowing about. If your assistant gives you an answer that turns out to be wrong because a page is outdated or incomplete, you can ask it to report that, and the correction reaches the docs team with the page path attached.

​
Bulk formats

If you are building a tool, seeding a knowledge base, or feeding a long-context model, three formats are published.

Page as Markdown

Append .md to any documentation URL to get that single page as clean Markdown.

llms.txt

An index of the documentation with a one-line description per page. Good for routing and discovery.

llms-full.txt

The complete documentation as a single Markdown file. Large, and intended for ingestion rather than browsing.

Sitemap

https://docs.flowx.ai/sitemap.xml lists every indexed page URL.
Example, fetching one page as Markdown:

​
Working across versions

FlowX.AI documentation is published as one tree per release family, and the version is part of every URL. When you ask a version-specific question, name the release in your prompt so the assistant searches and cites the right tree.
Ask for the version you are running, and check the version in the links your assistant returns. A question asked without a version can be answered from any tree, including release families that have reached end of life.
Feature Release content documented in the current LTS tree carries a SaaS availability badge. Those features are live on managed deployments immediately, and reach self-hosted deployments with the next LTS release family. If your assistant reports a feature as available, confirm against that badge before planning a self-hosted rollout.

MCP integration overview

Configure an MCP Server as a data source inside a FlowX project

Glossary

Core FlowX.AI terms, so your prompts use the right vocabulary

Build your first process

Start here if you are new to the platform

Knowledge Base and RAG

Build retrieval into your own FlowX agents
Last modified on August 20, 2026

Related topics

AI AssistantAI in FlowXFlowX.AI architectureUsing AI agentsConfig-time agents