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An MCP server makes a capability—such as a tool, resource, or prompt—available to an AI application through the Model Context Protocol. To build a first server, choose a current SDK for a language you already use, define one focused capability with an explicit schema, select the transport your intended host supports, and test both valid and invalid calls.
This guide follows the official TypeScript, Python, and Go starting points, then shows how to inspect a server locally and what to verify before sharing it. SDK APIs and host workflows change, so match tutorials to the SDK version they document.
What an MCP server does
MCP is an open standard for connecting AI applications to systems where tools and data live. The server exposes capabilities; the host connects to the server and makes those capabilities available to a model. As the official TypeScript SDK documentation puts it, “The MCP connects AI applications to the systems where your tools and data live; you build one side, a host brings the model.”
A server is not the AI model or the host application. It is the service or process that describes capabilities and handles requests for them. For a first implementation, keep the scope narrow: one tool that does one recognizable job is easier to describe, authorize, test, and debug than a tool with unrelated modes.
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Choose a language and SDK version
There is no documented benchmark or universal beginner choice among these SDKs. Start with the language you already use, then check the SDK and transport expected by the host you plan to connect. In particular, do not paste imports from an older tutorial into a newer SDK without checking its version.
| Starting point | What the official guide establishes | Local run or test path |
|---|---|---|
| TypeScript | The current v2 documentation uses @modelcontextprotocol/server, with stdio helpers under @modelcontextprotocol/server/stdio. It says v2 replaces the monolithic v1 package @modelcontextprotocol/sdk, and supports Node.js, Bun, and Deno. The page identifies its specification implementation as 2026-07-28. |
The cited v2 overview documents a one-file stdio server pattern. Follow the version-matched page for the precise setup and run commands. |
| Python | The official getting-started sequence covers SDK installation, building a server, connecting it to a host, and testing with an in-memory client. | Run uv run mcp dev server.py to open the server in MCP Inspector; the docs also show direct in-memory tests. |
| Go | The official quick start installs github.com/modelcontextprotocol/go-sdk/mcp, creates an mcp.Server, and registers a tool. |
The sample runs with mcp.StdioTransport and demonstrates a client launching the server process over stdin/stdout. |
| OpenAI integration example | OpenAI’s server guidance lists official TypeScript and Python SDKs. Its UI quickstart demonstrates a Node server using Streamable HTTP at /mcp. |
Run the server, then connect MCP Inspector to its local /mcp URL using Streamable HTTP. |
Sources: TypeScript SDK documentation, Python getting started, Go quick start, OpenAI server guidance, and OpenAI quickstart. The materials do not establish a performance ranking between languages.
Design one useful capability
Begin with a user goal, not an implementation trick. A focused tool has an action-oriented name, a description that tells the model when to call it, and a schema that makes acceptable inputs explicit. If the result is structured data, define its output shape too. Tool names and metadata help a model select and call the right capability.
- Use separate tools for distinct actions rather than a single tool with unrelated modes.
- Keep the result useful without requiring a custom UI component; stable identifiers make later calls against the same records easier.
- Make the handler enforce authorization, especially for private data and operations that change state. Metadata is not a security boundary.
- Use accurate safety annotations. Do not describe a write operation as read-only.
- If tools have cross-tool requirements, such as a required call order or shared rate limits, state them in server instructions. The OpenAI guidance recommends keeping key instructions within the first 512 characters.
A simple first tool might accept a city name and return a forecast, or accept a record identifier and return a concise status. Keep validation close to the boundary, and return errors that help the caller understand what needs correction without exposing secrets.
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TypeScript: follow the v2 stdio example
The documented v2 pattern creates an McpServer, registers a tool with a Zod input schema, and serves it over stdio. The SDK validates a call against the schema before the handler runs. Because the exact package entry points differ from v1, use the v2 page’s complete example and setup instructions rather than mixing in the older @modelcontextprotocol/sdk import names.
Stdio is appropriate when the host starts your server as a local process and exchanges protocol messages over standard input and output. Keep diagnostic logging off stdout so it cannot corrupt the protocol stream; use the SDK’s documented logging approach or stderr. Confirm the launch command and environment variables expected by the specific host.
Python: run the complete example in Inspector
Save the official Python example as server.py, install and configure the SDK as the getting-started page specifies, then run:
uv run mcp dev server.py
This opens MCP Inspector for interactive development. The Python guide also demonstrates testing with an in-memory client such as Client(mcp); this exercises tool calls directly without spawning a subprocess, opening a port, or testing a transport connection. It is useful for fast checks of handler behavior, but it does not replace testing the way your intended host launches or reaches the server.
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The Go quick start registers a tool on an mcp.Server and runs it through mcp.StdioTransport. Its example client connects to the server process using a command transport and calls the registered greet tool. Use the quick start’s complete setup and source rather than assuming its Go APIs match another SDK’s registration conventions.
OpenAI’s Streamable HTTP example
For the OpenAI UI quickstart, the example server exposes the MCP endpoint at http://localhost:<port>/mcp. Start that server, launch Inspector, choose Streamable HTTP, enter the local endpoint, and connect. This is a specific host-integration example, not a requirement that every MCP server use HTTP: stdio and Streamable HTTP have different launch and connection models.
Test the server before connecting a real workflow
Use MCP Inspector to confirm initialization and inspect which tools the server advertises. Then call each tool with both representative and invalid input. Check the schema, returned values, errors, and safety annotations. For tools that access private information or perform writes, verify authorization at the handler and test that unauthorized requests are rejected.
- Confirm initialization. Start the server using the intended command or endpoint and check that the host or Inspector completes its connection.
- Inspect advertised capabilities. Verify that names, descriptions, and schemas match the actual behavior.
- Exercise a normal call. Use realistic input and confirm the result contains the information the model needs for the next step.
- Try invalid and boundary input. Omit required fields, use the wrong type, and test values outside supported limits. Confirm validation or clear errors rather than silent incorrect output.
- Check permissions and side effects. Test allowed and denied users, and verify whether a tool reads, writes, or triggers an external action as described.
- Test the host’s actual connection path. An in-memory unit test verifies handler behavior; Inspector over stdio or HTTP verifies more of the transport and launch configuration.
The Python SDK documentation says its published examples are complete files under docs_src/ and are exercised by its test suite through an in-memory client. That statement applies to those examples, not to a new server you write. Run and inspect your own implementation.
Choose the transport that fits the host
Stdio and Streamable HTTP are not interchangeable run instructions. With stdio, a host typically starts a local process and communicates over its standard streams. With Streamable HTTP, the server listens at an endpoint and a client connects to it. Use the transport the target host supports, and test the exact connection path instead of inferring compatibility from a successful handler test.
For the OpenAI/ChatGPT quickstart, the documented server endpoint is /mcp; the page demonstrates connecting Inspector over Streamable HTTP and using an HTTPS tunnel or deployment URL for a development connection. Tunnel and developer-mode procedures can change, so check the current OpenAI quickstart when configuring that integration.
Common problems and fixes
- A copied TypeScript import cannot be resolved. The tutorial may target v1 while the code uses v2, or vice versa. Check whether it uses
@modelcontextprotocol/sdkor the v2@modelcontextprotocol/serverpackage, then keep package and imports aligned. - The host cannot start a stdio server. Check its configured executable, arguments, working directory, and environment. Run that same command directly in a terminal to expose missing dependencies or startup errors.
- Connection fails because the wrong transport was selected. Match the host configuration to the server: use process/stdio settings for a stdio example, or the correct Streamable HTTP endpoint for an HTTP example.
- Inspector connects but no tool appears. Confirm registration runs before the server starts, then inspect initialization output and the advertised tool list.
- A tool rejects an input that looks plausible. Compare the call against the declared schema, including required fields and types. Improve the description and schema if users could reasonably misunderstand the expected input.
- Protocol messages become corrupted by logs. For stdio servers, ensure ordinary log output is not written to stdout; reserve stdout for protocol traffic.
- A handler test passes but the host still fails. In-memory calls do not test process launch, transport configuration, or the host’s environment. Reproduce the failure with Inspector or the intended host’s connection method.
- Private data or writes are exposed too broadly. Add authorization inside the operation handler and test denied cases. Do not rely on tool descriptions or annotations to enforce access control.
Performance, reliability, and cost considerations
The cited official materials do not provide comparative performance benchmarks, latency guarantees, or a universal SDK ranking. For a first server, keep tool calls bounded, validate inputs early, and avoid returning unnecessary data. When an operation depends on an external system, make failures understandable and avoid claiming success until the operation has actually succeeded.
Test failure paths as well as the happy path: invalid parameters, unavailable downstream services, authorization failures, and interrupted connections where relevant. The transport you choose affects how a host launches or reaches the server, but the sources do not establish that one option is always faster or more reliable.
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Frequently Asked Questions
Does an MCP server include the AI model?
No. The server exposes capabilities; a host connects the server to an AI application and makes those capabilities available to a model.
Can I test a Python server without opening a port?
Yes. The Python SDK guide shows an in-memory client that calls the server directly; use a transport-level check as well when you need to verify how a host connects.
Does every MCP server need a public URL?
No. The TypeScript and Go examples include stdio, which runs as a process. A public or local HTTP endpoint is relevant when the chosen integration uses Streamable HTTP.
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