Flama makes MCP servers easier to build for AI agents
Flama lets turn ordinary Python functions into an MCP server for AI agents by adding a few decorators. MCP is an open standard that gives AI apps a common way to connect to outside abilities and data. A server can expose three things: tools, which are functions the AI can run; resources, which are data the AI can read; and prompts, which are reusable instruction templates.
In Flama, a server is registered at a URL path, then functions such as add or multiply can be exposed as tools, data can be exposed as a resource, and a summary template can be exposed as a prompt. Flama builds the input and output schema from Python type hints, so clients can see what arguments a tool needs and what result it returns. It also supports longer , extra user input during a tool call, and small shown beside a tool result.
One Flama app can host multiple , so teams can separate abilities by domain, version, or access level. Its stateless design means each request carries what it needs, which makes the server easier to scale across several machines.
Key points
- Flama exposes Python functions as MCP tools with decorators.
- MCP gives AI agents a shared way to discover and call tools, resources, and prompts.
- Python type hints are used to create the input and output schema automatically.
- Flama supports , extra user input, and small for richer tool flows.
- A single Flama app can run multiple for separate groups of abilities.