Wger Agent

CLI or API | MCP | Agent

Version: 2.0.0

Documentation — Installation, deployment, usage across the MCP, API, and agent interfaces, and guidance for provisioning the Wger Workout Manager platform are maintained in the official documentation.

Overview

Wger Agent is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Wger Workout Manager — exercise database, workout routines, nutrition plans, body measurements, and progress tracking..

Key Features

Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

CLI or API

This agent wraps the Wger Workout Manager — exercise database, workout routines, nutrition plans, body measurements, and progress tracking. API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

Auto-generated from the live MCP server — do not edit by hand.

Condensed action-routed tools (`MCP_TOOL_MODE=condensed`)

MCP Tool	Toggle Env Var	Description
`wger_body`	`BODYTOOL`	Manage wger body operations.
`wger_exercise`	`EXERCISETOOL`	Manage wger exercise operations.
`wger_ingest`	`INGESTTOOL`	Natively ingest wger records into epistemic-graph as typed nodes.
`wger_nutrition`	`NUTRITIONTOOL`	Manage wger nutrition operations.
`wger_routine`	`ROUTINETOOL`	Manage wger routine operations.
`wger_routineconfig`	`ROUTINECONFIGTOOL`	Manage wger routineconfig operations.
`wger_user`	`USERTOOL`	Manage wger user operations.
`wger_workout`	`WORKOUTTOOL`	Manage wger workout operations.

Verbose 1:1 API-mapped tools (`MCP_TOOL_MODE=verbose` or `both`)

132 per-operation tools — one per public API method (click to expand)

MCP Tool	Toggle Env Var	Description
`wger_create_day`	`APITOOL`	Create a workout day in a routine.
`wger_create_max_repetitions_config`	`APITOOL`	Create a max repetitions config.
`wger_create_max_rest_config`	`APITOOL`	Create a max rest config.
`wger_create_max_rir_config`	`APITOOL`	Create a max RiR config.
`wger_create_max_sets_config`	`APITOOL`	Create a max sets config.
`wger_create_max_weight_config`	`APITOOL`	Create a max weight config.
`wger_create_meal`	`APITOOL`	Create a meal in a nutrition plan.
`wger_create_meal_item`	`APITOOL`	Add an ingredient to a meal.
`wger_create_measurement`	`APITOOL`	Log a body measurement.
`wger_create_measurement_category`	`APITOOL`	Create a measurement category.
`wger_create_nutrition_diary_entry`	`APITOOL`	Log a nutrition diary entry.
`wger_create_nutrition_plan`	`APITOOL`	Create a nutrition plan.
`wger_create_repetitions_config`	`APITOOL`	Create a repetitions config.
`wger_create_rest_config`	`APITOOL`	Create a rest time config.
`wger_create_rir_config`	`APITOOL`	Create a RiR config.
`wger_create_routine`	`APITOOL`	Create a new routine.
`wger_create_sets_config`	`APITOOL`	Create a sets config.
`wger_create_slot`	`APITOOL`	Create a slot (set) in a day.
`wger_create_slot_entry`	`APITOOL`	Create a slot entry (add exercise to slot).
`wger_create_weight_config`	`APITOOL`	Create a weight progression config.
`wger_create_weight_entry`	`APITOOL`	Log a body weight entry.
`wger_create_workout_log`	`APITOOL`	Create a workout log entry.
`wger_create_workout_session`	`APITOOL`	Create a workout session. Impression: 1=General discomfort, 2=Could be better, 3=Neutral, 4=Good, 5=Perfect.
`wger_delete_day`	`APITOOL`	Delete a day.
`wger_delete_meal`	`APITOOL`	Delete a meal.
`wger_delete_meal_item`	`APITOOL`	Delete a meal item.
`wger_delete_measurement`	`APITOOL`	Delete a measurement.
`wger_delete_measurement_category`	`APITOOL`	Delete a measurement category.
`wger_delete_nutrition_diary_entry`	`APITOOL`	Delete a nutrition diary entry.
`wger_delete_nutrition_plan`	`APITOOL`	Delete a nutrition plan.
`wger_delete_repetitions_config`	`APITOOL`	Delete a repetitions config.
`wger_delete_rest_config`	`APITOOL`	Delete a rest config.
`wger_delete_rir_config`	`APITOOL`	Delete a RiR config.
`wger_delete_routine`	`APITOOL`	Delete a routine.
`wger_delete_sets_config`	`APITOOL`	Delete a sets config.
`wger_delete_slot`	`APITOOL`	Delete a slot.
`wger_delete_slot_entry`	`APITOOL`	Delete a slot entry.
`wger_delete_weight_config`	`APITOOL`	Delete a weight config.
`wger_delete_weight_entry`	`APITOOL`	Delete a weight entry.
`wger_delete_workout_log`	`APITOOL`	Delete a workout log.
`wger_delete_workout_session`	`APITOOL`	Delete a workout session.
`wger_get_day`	`APITOOL`	Get a specific day.
`wger_get_days`	`APITOOL`	List all workout days.
`wger_get_deletion_log`	`APITOOL`	List deletion log entries.
`wger_get_equipment`	`APITOOL`	List equipment (e.g., Barbell, Dumbbell, etc.).
`wger_get_equipment_item`	`APITOOL`	Get a specific equipment.
`wger_get_exercise`	`APITOOL`	Get a specific exercise.
`wger_get_exercise_aliases`	`APITOOL`	List exercise aliases.
`wger_get_exercise_categories`	`APITOOL`	List exercise categories (e.g., Arms, Legs, etc.).
`wger_get_exercise_category`	`APITOOL`	Get a specific exercise category.
`wger_get_exercise_comments`	`APITOOL`	List exercise comments.
`wger_get_exercise_image`	`APITOOL`	Get a specific exercise image.
`wger_get_exercise_images`	`APITOOL`	List exercise images.
`wger_get_exercise_info`	`APITOOL`	Get detailed exercise info (includes translations, images, muscles, etc.).
`wger_get_exercise_infos`	`APITOOL`	List exercise infos.
`wger_get_exercise_translations`	`APITOOL`	List exercise translations.
`wger_get_exercise_videos`	`APITOOL`	List exercise videos.
`wger_get_exercises`	`APITOOL`	List exercises. Supports filters: language, category, muscles, equipment, etc.
`wger_get_gallery`	`APITOOL`	List progress gallery images.
`wger_get_ingredient`	`APITOOL`	Get a specific ingredient.
`wger_get_ingredient_images`	`APITOOL`	List ingredient images.
`wger_get_ingredient_info`	`APITOOL`	Get detailed ingredient info (includes weight units).
`wger_get_ingredient_weight_units`	`APITOOL`	List ingredient weight units.
`wger_get_ingredients`	`APITOOL`	List ingredients. Supports filters: language, name, etc.
`wger_get_languages`	`APITOOL`	List available languages.
`wger_get_licenses`	`APITOOL`	List content licenses.
`wger_get_max_repetitions_configs`	`APITOOL`	List max repetitions configs.
`wger_get_max_rest_configs`	`APITOOL`	List max rest configs.
`wger_get_max_rir_configs`	`APITOOL`	List max RiR configs.
`wger_get_max_sets_configs`	`APITOOL`	List max sets configs.
`wger_get_max_weight_configs`	`APITOOL`	List max weight configs.
`wger_get_meal`	`APITOOL`	Get a specific meal.
`wger_get_meal_item`	`APITOOL`	Get a specific meal item.
`wger_get_meal_items`	`APITOOL`	List meal items.
`wger_get_meals`	`APITOOL`	List meals.
`wger_get_measurement`	`APITOOL`	Get a specific measurement.
`wger_get_measurement_categories`	`APITOOL`	List measurement categories (e.g., Biceps, Chest, etc.).
`wger_get_measurement_category`	`APITOOL`	Get a specific measurement category.
`wger_get_measurements`	`APITOOL`	List body measurements.
`wger_get_muscle`	`APITOOL`	Get a specific muscle.
`wger_get_muscles`	`APITOOL`	List muscles.
`wger_get_nutrition_diary`	`APITOOL`	List nutrition diary entries.
`wger_get_nutrition_plan`	`APITOOL`	Get a specific nutrition plan.
`wger_get_nutrition_plan_info`	`APITOOL`	Get detailed nutrition plan info (includes meals, items, nutritional values).
`wger_get_nutrition_plans`	`APITOOL`	List nutrition plans.
`wger_get_public_templates`	`APITOOL`	List public workout templates.
`wger_get_repetition_units`	`APITOOL`	List repetition unit settings.
`wger_get_repetitions_configs`	`APITOOL`	List repetitions configs.
`wger_get_rest_configs`	`APITOOL`	List rest time configs.
`wger_get_rir_configs`	`APITOOL`	List RiR configs.
`wger_get_routine`	`APITOOL`	Get a specific routine by ID.
`wger_get_routines`	`APITOOL`	List all routines.
`wger_get_sets_configs`	`APITOOL`	List sets configs.
`wger_get_slot`	`APITOOL`	Get a specific slot.
`wger_get_slot_entries`	`APITOOL`	List all slot entries.
`wger_get_slot_entry`	`APITOOL`	Get a specific slot entry.
`wger_get_slots`	`APITOOL`	List all slots (sets).
`wger_get_template`	`APITOOL`	Get a specific template.
`wger_get_templates`	`APITOOL`	List user's workout templates.
`wger_get_trophies`	`APITOOL`	List available trophies.
`wger_get_user_profile`	`APITOOL`	Get the current user's profile.
`wger_get_user_statistics`	`APITOOL`	Get user statistics (workout count, etc.).
`wger_get_user_trophies`	`APITOOL`	List user's earned trophies.
`wger_get_variations`	`APITOOL`	List exercise variations.
`wger_get_weight_config`	`APITOOL`	Get a specific weight config.
`wger_get_weight_configs`	`APITOOL`	List weight progression configs.
`wger_get_weight_entries`	`APITOOL`	List body weight entries.
`wger_get_weight_entry`	`APITOOL`	Get a specific weight entry.
`wger_get_weight_unit_settings`	`APITOOL`	List weight unit settings.
`wger_get_weight_units`	`APITOOL`	List weight units.
`wger_get_workout_log`	`APITOOL`	Get a specific workout log.
`wger_get_workout_logs`	`APITOOL`	List workout logs.
`wger_get_workout_session`	`APITOOL`	Get a specific workout session.
`wger_get_workout_sessions`	`APITOOL`	List workout sessions.
`wger_search_exercises`	`APITOOL`	Search exercises.
`wger_update_day`	`APITOOL`	Update a day.
`wger_update_meal`	`APITOOL`	Update a meal.
`wger_update_meal_item`	`APITOOL`	Update a meal item.
`wger_update_measurement`	`APITOOL`	Update a measurement.
`wger_update_nutrition_plan`	`APITOOL`	Update a nutrition plan.
`wger_update_repetitions_config`	`APITOOL`	Update a repetitions config.
`wger_update_rest_config`	`APITOOL`	Update a rest config.
`wger_update_rir_config`	`APITOOL`	Update a RiR config.
`wger_update_routine`	`APITOOL`	Update a routine.
`wger_update_sets_config`	`APITOOL`	Update a sets config.
`wger_update_slot`	`APITOOL`	Update a slot.
`wger_update_slot_entry`	`APITOOL`	Update a slot entry.
`wger_update_user_profile`	`APITOOL`	Update user profile. Fields: age, height, gender, etc.
`wger_update_weight_config`	`APITOOL`	Update a weight config.
`wger_update_weight_entry`	`APITOOL`	Update a weight entry.
`wger_update_workout_log`	`APITOOL`	Update a workout log.
`wger_update_workout_session`	`APITOOL`	Update a workout session.

8 action-routed tool(s) · 132 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (intent default — the six verb-tools, granular set loaded on demand · condensed action-routed · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/usage.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
Environment Variables: Define standard environment variables:
- MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
- MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
HTTP SSE Request Headers: Pass custom headers during transport initialization:
- x-mcp-enabled-tools / x-mcp-disabled-tools
- x-mcp-enabled-tags / x-mcp-disabled-tags
HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
- ?tools=tool1,tool2
- ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

MCP Configuration Examples

Install the connector-focused [mcp] extra. Examples use wger-agent[mcp] to add FastMCP / FastAPI through agent-utilities[mcp]; the required Agent Utilities core still carries epistemic-graph[full]. The [agent-runtime] extra additionally enables model orchestration.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "wger-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "wger-agent[mcp]",
        "wger-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "intent",
        "BODYTOOL": "True",
        "EXERCISETOOL": "True",
        "NUTRITIONTOOL": "True",
        "ROUTINECONFIGTOOL": "True",
        "ROUTINETOOL": "True",
        "USERTOOL": "True",
        "WGER_ACCESS_TOKEN": "your_api_token_here",
        "WGER_INSTANCE": "https://wger.de",
        "WGER_TOKEN": "your_api_token_here",
        "WGER_URL": "http://localhost:8000",
        "WORKOUTTOOL": "True"
      }
    }
  }
}

Runtime references require an alias-aware launcher such as GraphOS. Other launchers must omit those entries and inject the resolved values through their own runtime secret boundary.

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "wger-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "wger-agent[mcp]",
        "wger-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "127.0.0.1",
        "PORT": "8000",
        "MCP_TOOL_MODE": "intent",
        "BODYTOOL": "True",
        "EXERCISETOOL": "True",
        "NUTRITIONTOOL": "True",
        "ROUTINECONFIGTOOL": "True",
        "ROUTINETOOL": "True",
        "USERTOOL": "True",
        "WGER_ACCESS_TOKEN": "your_api_token_here",
        "WGER_INSTANCE": "https://wger.de",
        "WGER_TOKEN": "your_api_token_here",
        "WGER_URL": "http://localhost:8000",
        "WORKOUTTOOL": "True"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "wger-mcp": {
      "url": "http://localhost:8000/wger-mcp/mcp"
    }
  }
}

Run a reviewed container image as a least-privilege stdio child (no listener or published port):

docker run -i --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges \
  --pids-limit=256 \
  --tmpfs /tmp:rw,noexec,nosuid,nodev,size=64m \
  -e TRANSPORT=stdio \
  -e MCP_TOOL_MODE=intent \
  -e BODYTOOL=True \
  -e EXERCISETOOL=True \
  -e NUTRITIONTOOL=True \
  -e ROUTINECONFIGTOOL=True \
  -e ROUTINETOOL=True \
  -e USERTOOL=True \
  -e WGER_ACCESS_TOKEN=your_api_token_here \
  -e WGER_INSTANCE=https://wger.de \
  -e WGER_TOKEN=your_api_token_here \
  -e WGER_URL=http://localhost:8000 \
  -e WORKOUTTOOL=True \
  registry.example.invalid/wger-agent@sha256:<digest> wger-mcp

For containerized network HTTP, supply an authenticated TLS ingress (or direct server TLS), exact MCP_ALLOWED_HOSTS, and an exact trusted-proxy CIDR policy through the operator-owned deployment profile. The generator does not emit an unauthenticated non-loopback listener.

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

Additional Deployment Options

wger-agent can run as a local stdio process or container, or behind a remote network boundary. The Deployment guide carries the detailed transport contract.

Local container — launch a reviewed immutable image as a least-privilege stdio child with no listener or published port.
Remote URL — connect through an operator-supplied authenticated HTTPS ingress. Keep its URL, outbound identity references, trust profile, and exact MCP_ALLOWED_HOSTS in AgentConfig.

Environment Variables

Package environment variables

Variable	Example	Description
`HOST`	`0.0.0.0`
`PORT`	`8000`
`TRANSPORT`	`stdio`	options: stdio, streamable-http, sse
`ENABLE_OTEL`	`True`
`OTEL_EXPORTER_OTLP_ENDPOINT`	`http://localhost:8080/api/public/otel`
`OTEL_EXPORTER_OTLP_PUBLIC_KEY`	secret-injected
`OTEL_EXPORTER_OTLP_SECRET_KEY`	secret-injected
`OTEL_EXPORTER_OTLP_PROTOCOL`	`http/protobuf`
`EUNOMIA_TYPE`	`none`	options: none, embedded, remote
`EUNOMIA_POLICY_FILE`	`mcp_policies.json`
`EUNOMIA_REMOTE_URL`	`http://eunomia-server:8000`
`WGER_URL`	`http://localhost:8000`	Wger instance base URL (preferred)
`WGER_INSTANCE`	`https://wger.de`	Wger instance base URL (fallback when WGER_URL is unset)
`WGER_TOKEN`	secret-injected	Wger API token (preferred auth)
`WGER_ACCESS_TOKEN`	secret-injected	Wger API token (fallback when WGER_TOKEN is unset)
`TLS_PROFILE`	`private-ca`	AgentConfig named transport profile
`TLS_PROFILE_REF`	`secret://transport/provider`	Direct runtime profile reference
`TLS_PROFILES_REF`	`secret://transport/catalog`	Named runtime profile catalog
`ROUTINETOOL`	`True`
`ROUTINECONFIGTOOL`	`True`
`EXERCISETOOL`	`True`
`WORKOUTTOOL`	`True`
`NUTRITIONTOOL`	`True`
`BODYTOOL`	`True`
`USERTOOL`	`True`

Inherited agent-utilities variables (apply to every connector)

Variable	Example	Description
`MCP_TOOL_MODE`	`intent`	Tool surface: `intent` \| `condensed` \| `verbose` \| `both`
`MCP_ENABLED_TOOLS`	—	Comma-separated tool allow-list
`MCP_DISABLED_TOOLS`	—	Comma-separated tool deny-list
`MCP_ENABLED_TAGS`	—	Comma-separated tag allow-list
`MCP_DISABLED_TAGS`	—	Comma-separated tag deny-list
`MCP_CLIENT_AUTH`	—	Outbound MCP child auth: `oidc-client-credentials` \| `basic` \| `none`
`OIDC_CLIENT_ID`	—	OIDC client id (service-account auth)
`OIDC_CLIENT_SECRET_REF`	`secret://identity/oidc-client-secret`	Runtime secret reference for the OIDC service account
`MCP_BASIC_AUTH_USERNAME`	—	HTTP Basic username (`MCP_CLIENT_AUTH=basic`)
`MCP_BASIC_AUTH_PASSWORD_REF`	`secret://identity/mcp-basic-password`	Runtime secret reference for HTTP Basic auth (`MCP_CLIENT_AUTH=basic`)
`DEBUG`	`False`	Verbose logging
`PYTHONUNBUFFERED`	`1`	Unbuffered stdout (recommended in containers)
`MCP_URL`	`http://localhost:8000/mcp`	URL of the MCP server the agent connects to
`PROVIDER`	`openai`	LLM provider for the agent
`MODEL_ID`	`gpt-4o`	Model id for the agent
`ENABLE_WEB_UI`	`True`	Serve the AG-UI web interface

25 package + 16 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

Every variable the server reads, grouped by purpose.

Connection & Credentials

Variable	Description	Default
`WGER_URL`	Base URL of the Wger Workout Manager instance (preferred)	`http://localhost:8000`
`WGER_INSTANCE`	Base URL fallback when `WGER_URL` is unset	`https://wger.de`
`WGER_TOKEN`	Wger API token (preferred auth)	—
`WGER_ACCESS_TOKEN`	Wger API token fallback when `WGER_TOKEN` is unset	—
`TLS_PROFILE` / `TLS_PROFILE_REF`	AgentConfig transport profile selector; peer verification is mandatory	—

MCP server / transport

Variable	Description	Default
`TRANSPORT`	`stdio`, `streamable-http`, or `sse`	`stdio`
`HOST`	Bind host (HTTP transports)	`0.0.0.0`
`PORT`	Bind port (HTTP transports)	`8000`
`MCP_TOOL_MODE`	Tool surface: `condensed`, `verbose`, or `both`	`condensed`
`MCP_ENABLED_TOOLS` / `MCP_DISABLED_TOOLS`	Comma-separated tool allow/deny list	—
`MCP_ENABLED_TAGS` / `MCP_DISABLED_TAGS`	Comma-separated tag allow/deny list	—
`PYTHONUNBUFFERED`	Unbuffered stdout (recommended in containers)	`1`

Tool toggles

Each action-routed tool can be disabled individually via its toggle env var (set to false). The full list is in the Available MCP Tools table above.

Variable	Description	Default
`ROUTINETOOL`	Enable the routine tool	`True`
`ROUTINECONFIGTOOL`	Enable the routine-config tool	`True`
`EXERCISETOOL`	Enable the exercise tool	`True`
`WORKOUTTOOL`	Enable the workout tool	`True`
`NUTRITIONTOOL`	Enable the nutrition tool	`True`
`BODYTOOL`	Enable the body-measurement tool	`True`
`USERTOOL`	Enable the user tool	`True`

Telemetry & governance

Variable	Description	Default
`ENABLE_OTEL`	Enable OpenTelemetry export	`True`
`OTEL_EXPORTER_OTLP_ENDPOINT`	OTLP collector endpoint	—
`OTEL_EXPORTER_OTLP_PUBLIC_KEY` / `OTEL_EXPORTER_OTLP_SECRET_KEY`	OTLP auth keys	—
`OTEL_EXPORTER_OTLP_PROTOCOL`	OTLP protocol (e.g. `http/protobuf`)	—
`EUNOMIA_TYPE`	Authorization mode: `none`, `embedded`, `remote`	`none`
`EUNOMIA_POLICY_FILE`	Embedded policy file	`mcp_policies.json`
`EUNOMIA_REMOTE_URL`	Remote Eunomia server URL	—

Agent CLI (full `[agent]` runtime only)

Variable	Description	Default
`MCP_URL`	URL of the MCP server the agent connects to	`http://localhost:8000/mcp`
`PROVIDER`	LLM provider (e.g. `openai`)	`openai`
`MODEL_ID`	Model id (e.g. `gpt-4o`)	`gpt-4o`
`ENABLE_WEB_UI`	Serve the AG-UI web interface	`True`

See .env.example for a copy-paste starting point.

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Set credentials
export WGER_URL="your_value"
export WGER_TOKEN="your_value"

# Run the agent server
wger-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  wger-agent-mcp:
    image: example/wger-agent:mcp
    container_name: wger-agent-mcp
    hostname: wger-agent-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  wger-agent-agent:
    image: example/wger-agent@sha256:<digest>
    container_name: wger-agent-agent
    hostname: wger-agent-agent
    restart: always
    depends_on:
      - wger-agent-mcp
    env_file:
      - ../.env
    command: [ "wger-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://wger-agent-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9004:9004"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9004/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/deployment.md.

Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Feature	Functionality	Enablement
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

Installation

Pick the extra that matches what you want to run:

Extra	Installs	Use when
`wger-agent[mcp]`	Connector-focused MCP server (`agent-utilities[mcp]` — FastMCP/FastAPI + `epistemic-graph[full]`)	You only run the MCP server (smallest install / image)
`wger-agent[agent]`	Agent runtime (`agent-utilities[agent-runtime,logfire]` — model orchestration + `epistemic-graph[full]`)	You run the integrated agent
`wger-agent[all]`	Everything (`mcp` + `agent` + `logfire`)	Development / both surfaces

# Connector-focused MCP server (includes the shared graph engine)
uv pip install "wger-agent[mcp]"

# Agent runtime (adds model orchestration to the shared graph engine)
uv pip install "wger-agent[agent]"

# Everything (development)
uv pip install "wger-agent[all]"      # or: python -m pip install "wger-agent[all]"

Container images (`:mcp` vs `:agent`)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

Image tag	Build target	Contents	Entrypoint
`example/wger-agent:mcp`	`--target mcp`	`wger-agent[mcp]` — connector-focused, includes `epistemic-graph[full]`; no model-orchestration stack	`wger-mcp`
`example/wger-agent@sha256:<digest>`	`--target agent` (default)	`wger-agent[agent]` — agent runtime, model orchestration + `epistemic-graph[full]`	`wger-agent`

docker build --target mcp   -t example/wger-agent:mcp    docker/   # connector-focused MCP server
docker build --target agent -t example/wger-agent:agent-local docker/   # agent runtime

docker/mcp.compose.yml runs the connector-focused :mcp server; docker/agent.compose.yml runs the agent (immutable agent digest) with a co-located :mcp sidecar.

Knowledge-graph database (`epistemic-graph`)

Both [mcp] and [agent] carry the epistemic-graph engine through the required Agent Utilities core dependency (epistemic-graph[full]). The [mcp] extra keeps the server connector-focused; [agent] additionally enables model orchestration. Local deployments can use the bundled engine. For production or shared state, run epistemic-graph as a dedicated database service and configure the runtime to use it. Deployment recipes (single-node + Raft HA), connection configuration, and architecture diagrams are documented in the epistemic-graph deployment guide.

Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

Page	Contents
Installation	pip, source, extras, prebuilt Docker image
Deployment	run the MCP and agent servers, Compose, Caddy + Technitium, env config
Usage	the MCP tools, the `WgerApi` client, the agent CLI
Backing Platform	deploy the Wger Workout Manager with Docker
Overview	the standardized agent-package pattern
Concepts	concept registry (`CONCEPT:WGER-*`)

Repository Owners

Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

Format code using ruff format .
Lint code using ruff check .
Validate type-safety with mypy .
Execute test suites using pytest

Deploy with `agent-utilities-deployment`

Provision this package with the consolidated agent-utilities-deployment workflow. It selects an installed-package, editable-source, or immutable-container path; records only runtime secret and TLS-profile references in AgentConfig; and runs doctor, registration, policy, observability, and rollback gates. Ask your agent to "deploy wger-agent with agent-utilities-deployment".

Install mode	Command
Installed package	`uv tool install "wger-agent[mcp]"`, then run `wger-mcp`
Editable source	`uv pip install -e ".[agent]"`, then run `wger-mcp`
Immutable container	deploy `registry.example.invalid/wger-agent@sha256:<digest>` through the operator-selected orchestrator

The repository embeds no deployment profile, credential value, certificate path, or environment-specific endpoint. Supply those at runtime through AgentConfig and the configured secret provider.

Governed capability contract

This package ships a compact canonical skill surface with specialist procedures kept as referenced workflows. The current MCP tools, skill metadata, connector_manifest.yml, ontology, mappings, shapes, fixtures, migrations, tool-schema fingerprints, and certification metadata form one versioned capability contract. Validate them together; do not rely on stale tool names or historical per-task skill wrappers.

Runtime endpoints, credentials, certificate trust, tenant identity, retention, and observability policy are deployment inputs and are never packaged values. See Configuration, trust, and privacy before enabling a network transport, connector ingestion, GraphOS delegation, or trace export.

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
.security		.security
.specify/specs		.specify/specs
docker		docker
docs		docs
scripts		scripts
tests		tests
wger_agent		wger_agent
.bumpversion.cfg		.bumpversion.cfg
.codespellignore		.codespellignore
.dockerignore		.dockerignore
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
connector_manifest.yml		connector_manifest.yml
mcp_config.json		mcp_config.json
mkdocs.yml		mkdocs.yml
opencode.json		opencode.json
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements.txt		requirements.txt
uv.lock		uv.lock

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Wger Agent

CLI or API | MCP | Agent

Overview

Key Features

CLI or API

MCP

Available MCP Tools

Condensed action-routed tools (MCP_TOOL_MODE=condensed)

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

Dynamic Tool Selection & Visibility

MCP Configuration Examples

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

Streamable-HTTP Transport (networked / production)

Additional Deployment Options

Environment Variables

Package environment variables

Inherited agent-utilities variables (apply to every connector)

Connection & Credentials

MCP server / transport

Tool toggles

Telemetry & governance

Agent CLI (full [agent] runtime only)

Agent

Running the Agent CLI

Docker Compose Orchestration

Security & Governance

Access Control & Policy Enforcement

Runtime Security Grid

Installation

Container images (:mcp vs :agent)

Knowledge-graph database (epistemic-graph)

Documentation

Repository Owners

Contribute

Deploy with agent-utilities-deployment

Governed capability contract

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