MCP Server Frameworks: Choose by Tools, Transport, and Control
The right framework for building an MCP server depends on your language, the transport you need, how much control you want over schemas, and how the server will run in production. This guide compares the main options and explains what matters most for a first build.
Updated 5 min read
An MCP server framework handles the protocol machinery — initialization, JSON-RPC message routing, transport setup — so you can focus on the tools your server provides. The best choice is usually the one in the language your team already uses, with official SDK support and active maintenance.
Framework choices have downstream effects. They determine which transports you can use, how input schemas are defined, and how easy it is to test and deploy. Keeping the framework decision in a shared record, along with the reasons behind it, saves the next person from relitigating a choice that was already made. Tools like fenbs keep those decisions where every connected AI reads them first.
What to look for in a framework
Start with four questions. First: does the framework support the transport you need? Stdio is required for local servers that clients start as a child process; Streamable HTTP is required for remote servers. Some frameworks support both; some only support one.
Second: how does the framework define tool schemas? Some use code-first type annotations that generate JSON Schema automatically. Others expect you to write the schema by hand. Code-first generation is less error-prone; hand-written schemas give more explicit control.
Third: how active is the maintenance? The MCP specification is still evolving. A framework that tracks the spec closely will handle protocol version negotiation, new optional capabilities, and breaking changes better than one that has fallen behind.
Fourth: how does it behave in production? A server that runs fine in development but leaks connections, crashes under load, or is hard to monitor will be a problem. Check the framework's documentation for deployment guidance before you commit to it.
Comparison of main approaches
| Framework / approach | Language | Transport support | Schema definition | Best for |
|---|---|---|---|---|
Official TypeScript SDK (@modelcontextprotocol/sdk) | TypeScript / Node.js | stdio + Streamable HTTP | Code-first with Zod or TypeScript types | Teams already in the JS/TS ecosystem; well-maintained by the spec authors |
Official Python SDK (mcp) | Python | stdio + Streamable HTTP | Code-first with Python type hints | Teams already in Python; strong data and ML ecosystem fit |
| FastMCP (Python) | Python | stdio + Streamable HTTP | Decorator-based, less boilerplate | Rapid prototyping in Python; simpler API surface than the official SDK |
| Community Go libraries | Go | stdio + Streamable HTTP (varies by library) | Struct tags and interfaces | Teams in Go who need high concurrency or small binaries |
| Direct JSON-RPC (no framework) | Any | Any you implement | Hand-written | Unusual transports or languages with no SDK; adds significant maintenance work |
| fenbs | Connects over MCP (not a framework) | Streamable HTTP | Defined by the service | When the tool you need is shared project context: tasks, decisions, rules and lessons |
Official TypeScript SDK
The TypeScript SDK, published at modelcontextprotocol.io/sdks (opens in a new tab), is maintained by the team that writes the specification. It tracks the spec closely and supports both stdio and Streamable HTTP transports out of the box.
Tool schemas are defined using Zod or raw TypeScript types, and the SDK generates the JSON Schema the client reads automatically. Input validation runs before your handler is called, so you get typed, validated parameters rather than raw JSON.
The TypeScript SDK is the right default for most teams. It is the most widely tested, has the most examples in the community, and its documentation is updated when the spec changes. If you are not already in the Python or Go ecosystem, start here.
Official Python SDK
The Python SDK follows the same patterns as the TypeScript version. Tools are registered with Python type annotations, and the SDK generates schemas from them. It supports both transports and is maintained alongside the TypeScript SDK.
Python is a natural fit when your server wraps a data pipeline, a machine learning model, or an existing Python library. The ecosystem for database access, data processing, and API clients in Python is broad, and the SDK integrates well with standard Python tooling.
FastMCP is a popular community wrapper that adds a decorator-based API on top of the official SDK. It reduces boilerplate for simple tools and is a reasonable choice for rapid prototyping. For production use, confirm the underlying version of the official SDK it depends on is current.
Record the framework choice before you build
Add the framework name, version, and the reasons your team chose it as a Decisions and rules entry in fenbs. Include the transport type, the clients you intend to support, and the date. When the spec or the framework updates, revisit the decision and note what changed.
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Go community libraries
Go does not have an official MCP SDK from the specification team, but community libraries exist. They vary in spec coverage and maintenance activity, so check the repository's last commit date and which protocol version it targets before starting a project on one.
Go's strengths — small binaries, low memory use, good concurrency — make it appealing for servers that need to handle many simultaneous connections or run in constrained environments. For a single-user stdio server, those advantages are less important than the ecosystem maturity the official SDKs provide.
See mcp server golang for a practical guide to building an MCP server in Go.
When to skip a framework entirely
Writing a server directly against the JSON-RPC protocol — without an SDK — is rarely the right approach. You take on maintenance of the initialization handshake, capability negotiation, transport handling, and schema generation. That is significant work to do correctly and keep current as the spec evolves.
A reasonable exception is a language with no existing SDK that your team cannot avoid. In that case, implement only what you need, write thorough tests for the initialization and tool-call paths, and revisit periodically when new SDKs appear.
Testing and deployment considerations
Most frameworks provide a way to run the server in a test mode. Use the MCP Inspector, available from the modelcontextprotocol.io repository, to test any server without a full client setup. It connects over stdio, calls your tools directly, and shows raw responses.
For deployment, stdio servers are simpler: the client manages the process lifecycle. Streamable HTTP servers need a persistent host — a container, a serverless function, or a long-running process — and authentication for remote clients. See mcp server hosting for the deployment side.
Keep a note of which client versions you have tested against. A framework that passes the MCP Inspector may still behave unexpectedly with a specific client version. Record the combinations that work so your team does not have to rediscover them.
How to do this with fenbs
- Create a free account and set up a project for the MCP server work.
- Add the framework choice as a Decisions and rules entry: the name, version, transport, and reasoning.
- Build a small proof of concept with the chosen framework before committing to it. Record what worked and what did not as a Lesson linked to the decision.
- When the framework releases an update that changes behavior, check the decision and update or supersede it.
- Connect your AI to fenbs over MCP from Integrations so it reads the current framework decisions before working on the server code.