Developers are using AI clients for help with your product, but those assistants might not be giving them the right answers. When developers use Claude or Cursor while coding, these tools need real-time access to your documentation, API schemas, and repositories. Without an MCP server connecting your docs to AI clients, developers get responses based on outdated training data instead of your current product information. You're left maintaining documentation that's invisible to the tools developers actually use.
TLDR:
- MCP servers let Cursor, Claude, and other AI clients query your docs in real time
- 90% of organizations will use MCP by end of 2025 as AI-generated code reaches 41% of all code.
- MCP servers give AI agents direct access to your actual documentation as the source of truth.
- MCP reduces time to first API call by answering questions inside the developer's workflow.
- Every Fern docs site gets a free, hosted MCP server at
your-site.com/_mcp/server.
What MCP servers are and why they matter
AI clients like Cursor, Windsurf, and Claude Desktop have become primary development environments. Developers research APIs, write integration code, and debug errors, all without leaving their editor.
Model Context Protocol (MCP) is an open standard for directly connecting AI clients to external data sources. MCP servers expose your documentation, API schemas, and code repositories in a format that AI clients can query.
For documentation sites, MCP servers turn static web pages into structured, queryable resources. When a developer asks Claude about your API, the AI pulls current information directly from your docs instead of relying on stale training data. Your docs become a live data source that AI clients can access in real time.
Without MCP, your documentation becomes a reference developers have to context-switch to find, exactly the friction AI-assisted development was meant to eliminate.
Why AI agent need direct access to your documentation
Your product changes constantly with new endpoints, updated authentication flows, and revised SDKs. But AI assistants like Claude and Cursor can't automatically know about your latest release, your new API version, or the breaking changes you shipped last week. Without direct access to your current documentation, they default to whatever information they have, which might be accurate, outdated, or incomplete.
MCP servers connect AI assistants directly to your documentation as the source of truth. When your API changes, developers using Claude, Cursor, and other clients immediately get correct information. When you deprecate an endpoint, AI assistants stop recommending it. Your documentation becomes the live reference that AI tools query in real time, with no training updates required. 90% of organizations will use MCP by the end of 2025 because they need AI systems accessing current, verified information.
MCP adoption is accelerating across major AI clients and platforms
Developers have moved their workflows into AI clients. 82% of developers now use AI coding assistants daily or weekly, and 41% of all code is AI generated or assisted. Developers are working inside AI clients like Claude Desktop, Cursor, and Windsurf for entire coding sessions.
Your competitors are implementing MCP servers, and developers increasingly expect documentation to be AI-accessible. Without an MCP server, you're invisible when engineers ask AI assistants to compare tools or generate integration code.
The pattern is familiar. API companies that offered SDKs had an advantage over those requiring manual REST calls. Companies with interactive docs converted better than those with static pages. MCP is following the same trajectory, becoming expected infrastructure rather than a nice-to-have feature.
Without an MCP server, you're asking developers to context-switch out of their AI workspace to read your docs in a browser. That friction costs you integrations.
How MCP servers reduce developer time to first successful API call
Time to first successful API call matters more than almost any other metric for developer adoption. The faster someone can go from reading your docs to making a working request, the more likely they'll choose your API over alternatives.
MCP servers collapse this timeline by removing context switching. A developer asks Claude "how do I authenticate with this API?" and gets the exact auth method, required headers, and a working code snippet without opening a browser. They ask about required parameters for an endpoint and the AI pulls current information directly from your spec.
The onboarding steps that typically require documentation hunting happen inside the conversation:
- Authentication setup walks through your auth flow with actual spec details, not hallucinated examples.
- Required versus optional parameters come straight from your OpenAPI definition with type information.
- Error codes get explained using your documentation's descriptions, not generic HTTP status definitions.
- Rate limit handling reflects your actual limits and retry logic.
Support tickets drop because developers get answers before hitting errors. When someone asks about a 401 response, the AI explains your auth requirements using your actual documentation. Questions that would have become Slack messages or GitHub issues get resolved in the coding session.
This faster path to success directly affects conversion. When developers evaluate multiple APIs, they integrate with whichever has the smoothest onboarding experience.
Building your own MCP server versus automated solutions
Building an MCP server from scratch requires implementing JSON-RPC 2.0 protocol handlers, setting up hosting infrastructure, and writing custom code to serialize your documentation into MCP's expected format. You'll need to handle authentication between the MCP server and AI clients, implement error handling, and keep responses within token limits.
The bigger problem is maintenance. When your API changes, your team has to update the MCP server implementation to match. When the MCP spec evolves, you're adapting custom code. You're running a separate service just to expose documentation you already have.
Automated solutions generate MCP servers directly from your API specification. The same OpenAPI spec that powers your docs site also generates your MCP endpoint. When you update your spec, both artifacts regenerate automatically. Your documentation stays accessible to AI assistants without becoming another system to maintain.
Security and authentication considerations for MCP servers
MCP servers use your existing authentication to enforce the same access rules that protect your documentation site. When an AI agent connects via MCP, it authenticates using JWT tokens or OAuth flows that verify user identity before serving content.
Role-based permissions apply to AI agents just like human users. A developer with partner-tier access retrieves partner documentation through their AI assistant, while internal-only endpoints stay hidden from external MCP requests.
MCP server for your documentation site with Fern
Fern generates and hosts MCP servers at your-site.com/_mcp/server without requiring separate infrastructure or code. The server stays synchronized with your docs because they're built from the same source. You update your API definition once, and both human-readable docs and AI-accessible endpoints reflect the change.
Each docs page includes a "Connect to Cursor" button. Clicking it instantly connects Cursor's MCP client to your site's MCP server with the correct URL, capabilities, and metadata. No manual setup needed.
If your docs require authentication, Fern handles the entire flow. When developers connect, the MCP server issues JWTs through the Fern API, ensuring that agents and tools have proper, secure access to the parts of the docs they are allowed to see.
Fern makes your documentation interactive and tool-friendly by default. Your complete documentation set becomes part of the developer's coding environment.
Final thoughts on AI-accessible documentation
MCP servers turn your documentation into a live data source that AI assistants can query in real time. Developers expect to work inside their AI clients, and your documentation needs to meet them there. The question isn't whether to implement MCP: it's whether you'll maintain custom infrastructure or generate it automatically from the API specs you already have.
FAQ
What is an MCP server and why does my documentation site need one?
An MCP server exposes your documentation as a structured, queryable data source that AI clients like Cursor and Claude Desktop can access in real-time. Without one, AI assistants rely on outdated training data and generate code based on deprecated endpoints or incorrect authentication methods, causing developers to waste time debugging issues that stem from stale information rather than actual bugs.
How do I add an MCP server to my existing documentation?
Fern generates MCP servers automatically from your API specification at your-documentation-site.com/_mcp/server without requiring separate infrastructure. The server stays synchronized with your docs because both are built from the same OpenAPI spec. Updates to your API spec regenerate both artifacts through your existing CI pipeline.
Can I control which parts of my documentation are accessible through MCP?
Yes, MCP servers support the same authentication and role-based access control as your documentation site. When developers connect their AI assistant, the MCP server validates credentials and enforces permission restrictions, serving only the documentation sections they're authorized to view (such as partner-tier or internal-only content).
How does an MCP server reduce time to first successful API call?
MCP servers eliminate context switching by answering authentication questions, parameter requirements, and error handling directly inside the developer's AI client. Instead of opening a browser to search your docs, developers get current information from your actual API specification during their coding session, resolving questions that would otherwise become support tickets or integration blockers.
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