We Got Tired of LLMs Getting LeanMCP Wrong. So We Built Our Own Agent

It took 2 hours. It runs on our own platform. And it knows our docs better than ChatGPT.

Every time someone asked ChatGPT or Claude how to use LeanMCP, we'd wince. The answers were close enough to sound credible, but wrong enough to waste an hour of a developer's time.

So we did something about it.

The Problem

General-purpose LLMs have a knowledge cutoff. They know LeanMCP exists. They've seen some of our public docs. But they don't know the details - the specific package names, the exact API patterns, the two different ways OAuth works depending on your setup.

When someone asks "how do I add OAuth to my LeanMCP server?", ChatGPT gives one answer and hopes for the best. It might be right. It might be slightly wrong. You won't know until you try it and it breaks.

That's not good enough.

What We Built

We built an AI agent that lives on leanmcp.com. It has access to:

• LeanMCP docs
• LeanMCP blog posts
• Internal resources and changelogs

Ask it anything about LeanMCP and it gives you a cited, accurate answer - with links to the exact docs page it's referencing.

It took us 2-3 hours to build and deploy.

The Difference in Practice

Same question. Two very different answers.

Question: "How do I set up observability on LeanMCP?"

ChatGPT's answer was long, technically detailed, and spent half its response explaining how to set up monitoring on Vercel, AWS, GCP, and Azure - things that are completely irrelevant if you're deploying on LeanMCP Platform.

Our agent asked the right question first: "Observability on LeanMCP comes in two layers - which one do you mean?" Then it covered both, concisely, with the exact code snippet and a citation to the right docs page.

Question: "How do I add OAuth to my LeanMCP server?"

ChatGPT gave one OAuth path. Our agent recognized the ambiguity, explained two common scenarios, and gave complete working code for each - including the @Authenticated decorator pattern and the exact authUser.sub field you need downstream.

ChatGPT got close. Our agent got it right.

But First: How Do You Know It's Actually Accurate?

Before we shipped, we built a benchmark. 19 categories, ~130 questions - covering everything from onboarding to OAuth, pricing, deployment, debugging, enterprise qualification, and even investor questions. We ran every question through the agent and reviewed the results.

Zero errors across all 19 categories.

That's not luck. That's what happens when you build an agent on top of your own infrastructure, with your own knowledge base, and actually measure the output before you ship it.

How We Built It

We used our own platform to build this. Here's what it took:

1. Defined the knowledge base - LeanMCP docs, blog posts, and internal resources, structured so MCP tools can search them efficiently

2. Built MCP search tools - A few tools that can retrieve relevant content from different internal sources, with proper ranking and citation

3. Chose the model - We tested several. GPT ended up being the best fit for this use case: it handles web search well and produces clean, properly cited responses

4. Deployed on LeanMCP - The agent runs on our own infrastructure, using our own managed deployment platform

5. Plugged in observability - We use app.leanmcp.com to see every question users ask in real time. This tells us what people are confused about and where our docs need work

The whole thing - from idea to live - was an afternoon.

Why This Matters

This isn't just a customer support chatbot. It's proof that LeanMCP is the fastest way to take your internal knowledge and turn it into a deployed, observable AI agent.

We built ours in 2 hours. You can build yours for your own product, your own internal tools, your own customer base.

If you want to see what it looks like, try the agent on leanmcp.com

If you want to build something similar - especially with private data or custom integrations - talk to us

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