Everything between an idea and a production MCP.

Learn the server–client model and decide what capability the agent needs.

MCP defines a client-server protocol over JSON-RPC 2.0. A host application (an editor, a chat client) runs one or more clients, each holding a 1:1 connection to a server. A server exposes three primitive types: tools (functions the model can call), resources (data it can read), and prompts (reusable templates). Before writing code, decide which of the three actually fits — a common early mistake is building a "tool" for something that's really a resource, which changes how the client is expected to surface it to the user.

Select an official SDK or higher-level framework for your language and runtime.

The official SDKs (Python, TypeScript, Go, Java, C#, Rust) implement the protocol directly — you define handlers and the SDK manages transport, message framing, and capability negotiation. Higher-level frameworks like FastMCP add server composition, authentication providers, and OpenAPI-to-MCP generation on top. If you're wrapping an existing HTTP API, a framework that generates tools from an OpenAPI spec will save real time; if you're building bespoke logic, the official SDK gives you more direct control over how each tool's schema is described to the model.

Define clear tools, resources, prompts, inputs, outputs, and errors.

Every tool needs a name, a description written for the model (not for a human developer), and a JSON Schema for its inputs. The description is doing real work: it's what the model reads to decide whether and how to call the tool, so vague wording produces vague tool use. Define explicit error shapes too — a tool that just throws on failure gives the model nothing to reason about, while a structured error result lets it retry, ask the user, or pick a different tool.

Implement the server, choose a transport, and connect a real MCP client.

MCP supports two transports: stdio, for a server running as a local subprocess, and Streamable HTTP, for a remote server reachable over the network. Local stdio servers are simpler to build and debug first; a remote HTTP server adds real considerations around authentication and multi-user session state that don't exist locally. Test against an actual client early — Claude Desktop, Claude Code, or another MCP-aware client — rather than only against the SDK's own test harness, since client behavior around tool discovery and error display varies.

Inspect schemas, exercise failure paths, and validate what the model can discover.

Use the official MCP Inspector to see exactly what your server advertises — its full tool list, each schema, and the raw JSON-RPC messages — independent of any specific client's UI. Deliberately test failure paths: malformed input, a tool that times out, a resource that no longer exists. A server that only works on the happy path will produce confusing agent behavior in production, since the model has no way to recover gracefully from an error it wasn't designed to receive.

Choose hosting, lifecycle management, logs, traces, and evaluation.

Remote MCP servers can run on the same platforms as any HTTP service — the deployment question is usually about session state and cold starts, not MCP specifically. Once live, observability needs to go beyond generic request logs: you want visibility into which tools get called, with what arguments, and how often calls fail or return results the model didn't use — that's what tells you whether your tool descriptions are actually working.

Document the server and make it findable through registries and directories.

The official MCP Registry lets any server publish metadata under a namespace tied to a verified domain or package, so clients and other registries can discover it programmatically rather than through a hand-maintained list. Documentation matters twice here: once for the human deciding whether to install your server, and once for the model reading your tool descriptions at runtime — the second audience is easy to forget.