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Deploying as an MCP Server

Running as an MCP Server

fast-agent Can deploy any configured agents over MCP, letting external MCP clients connect via STDIO, SSE, or HTTP.

Additionally, there is a convenient serve command enabling rapid, command line deployment of MCP enabled agents in a variety of instancing modes.

This feature also works with Agent Skills, enabling powerful adaptable behaviours.

Using the CLI (fast-agent serve)

fast-agent serve [OPTIONS]

Key options:

  • --transport [http|sse|stdio] (default http)
  • --port / --host (for HTTP/SSE)
  • --instance-scope [shared|connection|request]– choose how agent state is isolated
    • shared (default) reuses a single agent for all clients
    • connection (sessions) Create one Agent per MCP session (separate history per client)
    • request (stateless) - create a new Agent for every tool call and disable MCP Sessions
  • --description – Customise the MCP tool description (supports {agent} placeholder)

Standard CLI flags also apply (e.g. --config-path, --model, --servers, --stdio, --quiet). This allows the fast-agent to serve any existing MCP Server in "Agent Mode", use custom system prompts and so on.

Examples:

fast-agent serve \
--url https://huggingface.co/mcp \  
--instance-scope connection \
--description "Interact with the {agent} workflow" \
--model haiku

This starts a Streamable HTTP MCP Server on port 8000, providing access to an Agent connected to the Hugging Face MCP Server using Anthropic Haiku.

fast-agent serve \
--npx @modelcontextprotocol/server-everything \  
--instance-scope request \
--description "Ask me anything!" \
-i system_prompt.md
--model kimi

This starts a Streamable HTTP MCP Server on port 8000, providing agent access to the STDIO version of the "Everything Server" with a custom system prompt.

Running an agent

If you already have an agent module or workflow (e.g. the generated agent.py), you can start it as a server directly:

uv run agent.py --server [OPTIONS]

The embedded CLI parser supports the same server flags as the serve command:

  • --transport, --host, --port
  • --instance-scope [shared|connection|request]
  • --description (tool instructions)
  • --quiet, --model, and other agent startup options

Example:

uv run agent.py \
--server \
--transport http \
--port 8723 \
--instance-scope request

Both approaches initialise FastAgent with the same config and skill loading pipeline; choose whichever fits your workflow (one-off CLI invocation vs. packaging an agent as a reusable script).