Customers are increasingly asking if your SaaS product works with AI assistants like Claude, ChatGPT, or Cursor. They want to perform real work inside your product without manually copying data between tabs. The solution is an official Model Context Protocol (MCP) server. Developer Fady Mondy has released a comprehensive guide on how to plan, build, and ship an official MCP server in approximately two weeks, provided you have a stable, documented API.

What Is an MCP Server?

An MCP server acts as a thin layer that sits beside your product and communicates with your existing API. It exposes a short list of tools, each with a name, plain-language description, and typed input schema. The AI assistant reads these descriptions to decide which tool fits the user's request. Crucially, your backend doesn't change; the server is simply a client of your API. You can build it in TypeScript, Go, or PHP, depending on what your team can maintain. There are two common deployment methods: remote (hosted HTTP endpoints with OAuth) and local (packages run on the user's machine).

Why 'Official' Matters

If you don't ship an official server, community versions may appear quickly. These often require users to paste full-access API keys into config files, meaning unvetted code runs with production credentials. This leads to support tickets and security concerns. An official server allows you to control which actions are exposed, how authentication works, and how your product is described to the AI assistant, ensuring a safer and more reliable user experience.

The Two-Week Plan

Mondy breaks the process into a realistic two-week sprint. Step 0 involves writing a one-page tool spec before any code is committed, listing proposed tools, inputs, and auth scopes. Days 1โ€“3 focus on tool design and scaffolding, emphasizing clear names like find_customer over generic CRUD endpoints. Days 4โ€“7 are dedicated to authentication and permissions, implementing OAuth 2.1 for remote servers and scoped API keys as fallbacks. It is critical to mark read-only tools separately from destructive ones to streamline agent permissions. Days 8โ€“10 involve testing against real assistants like Claude and Cursor to catch issues like wrong tool selection or excessive data return. The final phase, Days 11โ€“14, covers documentation, listing in MCP directories, and handover. Setup guides should be created for each assistant, and permissions explained in plain language. The repository and deployment notes are handed over with the code shipping under your name and license.

Key Takeaways

  • Tool design matters more than code volume; focus on the 10โ€“20 actions users actually request.
  • Use OAuth 2.1 for remote servers and implement scoped API keys with expiration dates.
  • Test against real AI assistants to refine descriptions and ensure proper tool selection.
  • Record agent actions distinctly (e.g., author_kind = 'agent') to maintain traceability.
  • A public, stable API is a prerequisite; complex multi-tenant permissions may extend the timeline.

The Bottom Line

This guide demystifies MCP integration for SaaS founders, proving that with a clear plan and existing API, you can deliver a secure, official AI integration in just two weeks.