July 2, 2026 · 7 min read
Developer tool companies have always competed on documentation. A great API with terrible docs loses to an okay API with great docs, every time. In 2026, the competition has a new dimension: your docs need to work for AI agents, not just humans.
Here's the concrete case for doing this, what it takes, and what to expect.
Your users are developers. Developers are early adopters of AI coding tools. They're asking Claude and Cursor questions about their dependencies all day. When they ask "how do I authenticate with your API?", one of two things happens:
1. Claude knows your API well enough to answer accurately (rare, especially for newer or smaller products).
2. Claude makes something up, gives an outdated answer, or tells them to check your docs — at which point they leave their IDE, go to your docs, find the answer, and come back.
Outcome 2 is friction. It's not the end of the world — they still found the answer — but it means your docs are doing extra work that they don't have to do. More importantly, it means your product is experiencing a quality gap relative to larger competitors whose APIs Claude knows better from training data.
The fix: make your docs queryable via MCP so Claude can get the right answer from your actual content, in real time.
A queryable docs site means: given a natural-language question, the system can find and return the relevant content from your docs. Not a link to a page — actual content, with a citation.
The underlying technology is semantic search — embedding your content and retrieving chunks based on query similarity. The interface for AI agents is MCP — a tool the agent can call with a domain and a question.
When a developer asks Claude "how do I handle rate limit errors in the Acme API?", Claude calls:
ask_site("acme.com", "how do I handle rate limit errors?")AgentReady retrieves the relevant chunks from your indexed docs and returns the answer. Claude cites the source. The developer has the answer without leaving Cursor.
Lower support load. Questions your docs currently answer — but that developers don't bother reading — start getting answered by Claude without anyone filing a ticket.
Better developer experience. Your API is easier to use when developers can ask questions conversationally instead of navigating docs to find the right section.
Competitive parity. Larger competitors like Stripe and Twilio will get their docs indexed whether they ask for it or not — their content volume is large enough to be in training data. Smaller products need to proactively make this happen. Indexed docs level the playing field.
Discovery. As the MCP ecosystem grows, directories of queryable sites will matter. Being in AgentReady's directory means developers who browse it see your product. It's a distribution channel, not just a UX improvement.
Step 1 (10 min): Write an llms.txt. Put a summary of your product and links to key docs at yourdomain.com/llms.txt. Helps with training-time awareness.
Step 2 (10 min): Index your docs. Go to agentready.it.com, submit your docs URL. 60 seconds to index.
Step 3 (20 min): Test it. Add AgentReady's MCP server to Claude Desktop or Cursor, ask a few common questions about your API, verify the answers are accurate and cited correctly.
Step 4 (20 min): Set up re-indexing. If you're on WordPress, install the AgentReady plugin. For other platforms, add a deploy hook that calls POST https://www.agentready.it.com/api/crawl with your docs URL.
The easiest sell internally is the support angle: "we're making it so Claude can answer questions about our API from our actual docs, so developers can get help without filing tickets". That's a clear, concrete outcome that's easy to understand and measure.
The bigger story — that the distribution channel for developer tools is shifting from Google to AI agents, and you want to be discoverable in that channel — is also true, but it's a longer-term bet that's harder to pitch to a skeptical PM. Start with the support angle. The distribution value shows up in analytics over time.