What is MCP and Why Should You Care?
AI is transforming the way internet and data is used. If your service has an API that delivers value to customers, now is the time to ensure it works with AI assistants. Soon enough, users will expect these integrations to just work with their digital assistant, be it ChatGPT, Claude, Gemini or Grok.
If you’ve got a REST API that’s been serving mobile apps and web clients, you might think you’re done. Your endpoints are documented (and Swagger’ed), your SDK works great, and developers know how to integrate. But AI assistants don’t work like human developers. They need context – what does each endpoint do? What does each parameter mean? How do people typically use this? Technical documentation here isn’t enough. You need Model Context Protocol (MCP)
What Is MCP?
MCP is a bridge between your technical API and conversational LLMs. It’s an additional layer that teaches AI assistants how to use your service the way a human would.
MCP gives you a way to explain your API in a language LLMs understand. You describe what each operation does, what each parameter means, and how they’re typically used. Your existing API stays unchanged – you’re just adding the conversational context that makes it understandable to LLMs.
What Can You Actually Do With This?
Once you have an MCP server layer for your API, several practical use cases open up:
Conversational Interfaces
MCP turns human language into an interface for your company’s data and workflows. It will enable you to talk to your data. You can connect your API MPC layer to an LLM powered chat interface and it can use your api as a tool to get information. A marketing analyst might ask, “Show me all new users who made purchases last month?” and receive a clear summary without the need to make SQL statements or trying to filter through data in an analytics dashboard. By having MCP AI can fetch, analyze and summarize data for you.
Intelligent Automation
Tools like n8n can use your MCP server to build workflows that understand context. Example: when a high-value lead fills out a contact form, automatically retrieve their company information from your CRM, summarize their past interactions, check if they match your ideal customer profile, and generate a personalized outreach email. The automation isn’t just moving data between systems – it’s understanding and acting on that data.
AI Ecosystem Integration
Your MCP server becomes a bridge between your service and the AI ecosystem. Partners can integrate your API into their AI tools. Developers can build AI applications on top of your platform. You’re not building custom integrations for every possible use case – you’re making your service AI-native, and others build on top of that.
How It Actually Works
Creating an MCP server means adding conversational explanations to your existing API – describing what operations do, what parameters mean, and providing helpful context.
Here’s a concrete example. Say you have an API for creating tasks:
What your API expects (technical): – project_id: a unique ID – title: task description – due_date: a date/time format – priority: a number from 1 to 5
What you tell the LLM (conversational): – project_id: “Which project should this task belong to? You can use the project name or ID.” – title: “What needs to be done? Be specific about the deliverable.” – due_date: “When should this be completed? Can be relative like ‘tomorrow’ or ‘next Friday’, or a specific date.” – priority: “How urgent is this? 1 = low, 3 = normal, 5 = critical”
Your API endpoint doesn’t change at all. But now when someone asks their AI assistant to “create a high-priority task for the mobile app project due next Friday,” the LLM understands: – “mobile app project” maps to project_id – “high-priority” means priority: 5 – “next Friday” needs to become a proper date format
Your MCP layer handles all the translation between natural language and technical requirements.
What About Performance and Security?
A few practical considerations for teams evaluating MCP:
Latency: MCP servers add minimal overhead since they’re just translating conversational requests into your existing API calls. There’s no heavy processing – just parameter mapping and context handling.
Security: Your existing security model stays intact. Authentication, rate limiting, and access control work exactly as they do now. The MCP layer sits in front of your API and respects all your existing protections.
Maintenance: Keeping your MCP layer in sync with API changes is straightforward. When you add or modify an endpoint, you update the conversational descriptions. You’re not reimplementing functionality – just updating the explanations.
Is This Hard to Build?
If you’ve built a REST API, you have the skills to add an MCP layer. The protocol is well-documented with working examples across different tech stacks. Most teams can get a basic MCP server running in a few days. The real work is thinking through how people will naturally talk to your API – what questions they’ll ask, what shortcuts make sense, what context helps the LLM make good decisions.
Companies are making these moves now. APIs with conversational bridges will have a significant advantage over those waiting for clarity.
Your API already works. MCP just teaches AI assistants how to use it.
Getting Started
Building an MCP server for your API is more accessible than you might think. The official MCP documentation provides clear patterns, and there are working examples across different tech stacks.
Looking for implementation guidance? Check out MCP Best Practices: Building Conversational APIs Right for detailed advice on context optimization, security, and smart API design.
Building your first MCP integration and want experienced guidance? I help companies design and implement AI-native APIs that work naturally with modern LLM assistants. Let’s talk about your use case.
Sources: – Model Context Protocol Specification – GitHub MCP Servers Repository – What is the Model Context Protocol (MCP)? A Complete Guide – Treblle – Model Context Protocol (MCP): A comprehensive introduction for developers – Stytch
