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.

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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..

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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-*)

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Repository Owners

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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.