How to build an MCP server in front of an existing platform API

What is an MCP server?

The Model Context Protocol (MCP) is an open protocol that standardizes how AI applications connect to external systems. An MCP server exposes capabilities, mainly tools, plus optional resources and prompts, to any MCP-compatible client, such as an AI assistant, an IDE or an agent framework. The client discovers what the server offers with tools/list and invokes a tool with tools/call, using JSON-RPC 2.0 messages.

For a company with an existing platform API, an MCP server is a thin, purpose-built layer in front of that API. It turns endpoints designed for developers into a short list of typed tools that a language model can choose between, fill in and call, so you do not write a separate integration for every assistant.

When does an MCP server make sense?

Build one when people or agents need answers or actions your API already supports, but the API expects precise parameters and users think in sentences. Common cases include:

• Customer-facing assistants that answer from live data instead of a static help center.
• Internal agents that query CRM, ticketing or admin systems as one step in a workflow.
• Product teams that want one AI entry point shared by chat, web and mobile surfaces.
• Partners or customers who bring their own AI assistant and want to connect it to your platform.

If most of the knowledge lives in unstructured documents rather than an API, retrieval is usually the better first step; see how to build an internal RAG system. The two combine well, since an MCP tool can wrap a retrieval index.

How do you build an MCP server, step by step?

1. Start from the questions. List the real requests users make and map each one to the API calls that answer it. That list defines the scope better than the API reference does.
2. Design a small tool surface. Each tool does one job and has a clear name, a description written for the model, and a JSON Schema for its inputs. Prefer search_tournaments with explicit filters over a generic call_endpoint tool.
3. Choose a transport. Use stdio for a local server that runs next to a desktop client, and Streamable HTTP for a remote server that many clients and applications reach over the network.
4. Implement with an official SDK. MCP SDKs for TypeScript, Python and other languages handle the handshake, capability negotiation and message framing, so your code only maps tool inputs to API calls.
5. Shape the outputs. Return compact, relevant fields instead of raw API payloads, because every token sent back to the model adds latency and noise. Offer structured output alongside text when applications, not just people, consume the results.
6. Test with real clients. Use the MCP Inspector or a similar tool, plus at least one real assistant, to confirm tools are discovered, chosen correctly and called with valid arguments.

Tool descriptions matter more than most teams expect. The model picks a tool and extracts its parameters from the name, description and schema, so vague wording shows up directly as wrong tool calls.

How do you secure an MCP server?

An MCP server gives a model a path into your systems, so treat it like any public API surface, with stricter defaults. Start read-only, and add write tools only where a use case requires them, scoped narrowly and preferably behind a confirmation step.

• Authentication: for remote servers, the MCP specification builds authorization on OAuth 2.1. Authenticate the calling client and, where data is user-specific, act on behalf of that user.
• Scoping: call the upstream API with the minimum permissions each tool needs, never with an admin key.
• Token handling: cache upstream access tokens and refresh them before they expire, so each tool call does not trigger a new auth round trip.
• Rate limits and timeouts: protect the platform API from a model that loops, and return clear errors the model can recover from.
• Input validation: check every argument against the schema and your business rules, because model-generated parameters are untrusted input.

Log every tool call with its inputs, outputs and latency. Tracing is how you debug a wrong answer, and how you show what the system actually did.

How do you know it works in production?

A demo that answers five questions is not evidence. Build an evaluation set of real queries with the expected tool and parameters for each, run it on every change to tool descriptions, prompts or models, and track tool-selection accuracy, argument validity and answer quality over time. Set latency and cost budgets per request and alert when they drift. Streaming responses improve perceived latency, because users see the answer forming while the server is still working. This is the same lifecycle discipline that MLOps applies to models.

How SigmaBit builds MCP servers

SigmaBit built Picklebot, an AI concierge for the Pickleball.com ecosystem, as an MCP server in front of the platform API. It registers six read-only tools covering tournaments, players, clubs, team leagues, ladder leagues and a support FAQ. A question asked in plain English is routed to the right tool, parameters such as rating, format, gender, location and status are extracted from the sentence, live data is queried, and the answer streams back in natural language.

A second mode returns the same result as structured JSON, so product surfaces can render cards instead of prose. Scoped authentication with cached tokens, location awareness and read-only access are built in. Picklebot is in development and being integrated into the Pickleball.com experience.

We reuse that pattern for internal systems: a typed tool surface, read-only by default with scoped writes where they are warranted, authentication and rate limits, streaming, and tracing on every tool call. One server can then answer a person in chat and an application over an API. Our AI automation and agents work covers the rest of the stack around it.