wger-agent¶

Wger Workout Manager MCP server + A2A agent for the agent-utilities ecosystem — exercise database, workout routines, nutrition plans, body measurements, and progress tracking, exposed as typed, deterministic tools.

Official documentation

This site is the canonical reference for wger-agent, maintained alongside every release.

Overview¶

wger-agent wraps the Wger Workout Manager REST API with action-routed MCP tools and a Pydantic-AI graph agent. It provides:

WgerApi — a unified Python client composed of domain-specific sub-clients (routine, exercise, nutrition, workout, body, user) over the Wger REST surface.
Action-routed MCP tools across seven togglable domains, registered through the agent-utilities FastMCP middleware to minimize token overhead in LLM contexts.
An integrated graph agent (the wger-agent console script) that speaks the Agent Control Protocol and the Agent Web UI, with a confidence-gated router that enables only the tools relevant to each request.

Explore the documentation¶

Installation — pip, source, extras, and the prebuilt Docker image.
Deployment — run the MCP and agent servers, Docker Compose, Caddy + Technitium.
Usage — the MCP tools, the WgerApi client, and the agent CLI.
Backing Platform — deploy the Wger Workout Manager with Docker.
Architecture — the standardized agent-package pattern.
Concepts — the CONCEPT:WGER-* registry.

Quick start¶

pip install wger-agent
wger-mcp                         # stdio MCP server (default transport)

Connect it to a Wger instance:

export WGER_URL=https://your-wger:8000
export WGER_API_KEY=your_api_key
wger-mcp --transport streamable-http --host 0.0.0.0 --port 8000

See Installation and Deployment for the full matrix (PyPI extras, Docker image, all transports, the agent server, reverse proxy, DNS).