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# How to Build a Documentation MCP Server

Learn how documentation MCP servers give AI coding assistants real-time, version-accurate access to docs, preventing broken code and saving developers hours of debugging.

by Bhargob Deka

## Overview

AI assistants are only as good as the documentation they can access. Unless they have an accurate, up-to-date reference point, they may hallucinate and produce outdated syntax and broken code. This is a common problem among developers.

According to a [survey by Stack Overflow](https://usestash.com/blog/why-developer-documentation-fails-without-context/), 61% of developers lose more than 30 minutes a day looking for specific solutions.

## Why Docs as MCP?

Imagine you’re building an agentic application with LangChain using Cursor. Here’s an example of code generated for you:

```python
from langchain.agents import create_react_agent
from langchain.tools import Tool
from langchain.chat_models import ChatOpenAI

# Create the agent
agent = create_react_agent(
    llm=ChatOpenAI(model="gpt-4"),
    tools=[search_tool, calculator_tool],
    memory=ConversationBufferMemory()
)

# Run the agent
response = agent.run("What is 15% of $100?")
```

But you encounter an error:

```
ImportError: cannot import name 'create_react_agent' from 'langchain.agents'
```

Then, you turn to ChatGPT or AI coding tools for help, but the code snippets they come up with aren’t up-to-date with the framework’s latest documentation. This is where documentation MCP servers come into play.

MCP is a standard way for AI agents to connect to external data and tools through an open and unified protocol. With documentation MCP servers, AI assistants can:
- Browse through all available documentation;
- Fetch specific document pages;
- Find the latest technical information in real time;
- Pull version-specific APIs.

## Real-World Examples

Tech giants like AWS and Microsoft have begun shipping and maintaining docs as MCP servers. Microsoft’s [Learn MCP server](https://learn.microsoft.com/en-us/training/support/mcp) provides access to their official technical documentation. Similarly, [AWS MCP server](https://docs.aws.amazon.com/aws-mcp/) gives developers access to documentation for various AWS services.

## Should You Build Your Own?

If you are considering building an MCP server, it requires significant engineering work, including:
1. **Content Ingestion**: Handling multiple formats of documentation.
2. **Chunking and Embedding**: Breaking content into chunks and converting them into vector embeddings.
3. **Deciding on a Database**: Choosing an appropriate storage solution for embeddings.
4. **Creating the Retrieval Pipeline**: Building a pipeline for accurate document retrieval.
5. **Implementing the MCP Server**: Including authentication layers and hosting.
6. **Monitoring the Application**: Continuously tracking the performance of various components.

## Building It with Kapa.ai

With Kapa.ai, you can launch a production-grade documentation server in just five steps:
- Log in to the Kapa dashboard.
- Click “Add new integration.”
- Select Hosted MCP server.
- Choose your sub-domain.
- Pick your auth type.

Your engineering team can stay focused on building your core business product while Kapa handles the entire pipeline for you, including support for 50+ document types and a full analytics dashboard.

## Build or Buy? It’s Up to You!

Building an MCP server makes sense if you have a dedicated ML engineering team comfortable with RAG systems, while a hosted solution like Kapa is ideal for automation. Most teams underestimate the complexity involved in building an MCP server and may end up with poor retrieval accuracy after months of development.

## FAQ
### What is a documentation MCP server?
A documentation MCP server provides real-time, version-accurate access to technical documentation that AI assistants can utilize.

### How does an MCP server work with AI coding assistants?
AI assistants query the MCP server to retrieve documentation as needed, supporting developers directly within their IDE.

### How long does it take to build an MCP server?
For an experienced ML engineering team, it can take at least 2-4 weeks. If balancing alongside other projects, it may take months.

### Should I build or buy a documentation MCP server?
Build if you have the expertise and resources; otherwise, use a hosted MCP solution to avoid engineering complexities.