What can you do with this server?

A stateless MCP server providing evidence-based strength training tools that encode classical powerlifting programs, fatigue modeling, and training science principles — with all state managed by the calling agent. * Browse training programs (list_training_templates): Explore a built-in library of classical programs (5/3/1, Texas Method, Madcow, GZCLP, nSuns CAP3, Coan-Philippi, Smolov Jr), filterable by category (powerlifting, strength, peaking) and difficulty (beginner, intermediate, advanced). * Retrieve weekly training plans (get_template_plan): Get a specific week's prescribed sessions from any template, including exercises, sets, reps, intensity, and AMRAP flags. * Look up exercise form guidance (lookup_exercise_form): Get form cues, common mistakes, and equipment-filtered alternatives for any exercise. * Learn training science principles (explain_principle): Get cited explanations of concepts like RPE autoregulation, periodization, the Banister model, deload triggers, and volume landmarks. * Calculate fatigue scores (calculate_fatigue_score): Compute Banister fitness-fatigue metrics (CTL/ATL/TSB) from recent training history, optionally incorporating recovery data like sleep and soreness. * Get session modification suggestions (suggest_session_modification): Receive actionable adjustment recommendations (scale weight, change intensity, deload, etc.) based on planned vs. actual performance and current fatigue state. * Apply plan adjustments (apply_plan_adjustment): Modify a week's plan by applying adjustments such as DELOAD_WEEK, SCALE_WEEK, SHIFT_VOLUME, or ADD_REST_DAY, returning the adjusted plan JSON. * Get today's session recommendation (recommend_session_for_today): Receive a personalized session recommendation with rationale based on your template, current week, fatigue state, and last session.

How do I use strength-training-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@strength-training-mcp Recommend today's session with fatigue score -15." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

strength-training-mcp

by daiduo2

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Strength Training MCP Server

A stateless MCP server exposing 8 tools for evidence-based strength training. Encodes classical powerlifting programs (5/3/1, Texas Method, Madcow, GZCLP, nSuns CAP3, Coan-Philippi, Smolov Jr), the Banister fitness-fatigue model, RPE-based autoregulation, and an adjustment policy engine.

No user data is stored on the server. All state lives in the calling agent. The server is a pure function: same inputs → same outputs.

Supported Transports

Transport	Support	Entry Point	Use Case
Stdio	✅	`strength-training-mcp`	Claude Desktop, Claude Code, Cursor
Streamable HTTP	✅	`strength-training-mcp-http` / `POST /mcp`	ModelScope, Aura, remote agents
SSE	❌	—	Not implemented (use Streamable HTTP instead)

Related MCP server: aiTrainer

Quick Start

Stdio (local clients)

uvx --from strength-training-mcp strength-training-mcp

HTTP server (remote / cloud)

uvx --from strength-training-mcp strength-training-mcp-http --port 8080

Test:

curl http://localhost:8080/health
# → {"status":"ok","version":"0.1.1"}

For MCP calls over HTTP, use any MCP client (e.g., fastmcp.Client, Claude Desktop, or ModelScope) pointing at http://localhost:8080/mcp.

Deploy on ModelScope

This package is published to PyPI as strength-training-mcp. ModelScope can deploy it directly via uvx.

Step 1 — Choose transport

In ModelScope MCP deployment console, select Stdio or Streamable HTTP.

Recommendation: Use Stdio for the simplest one-click deployment.

Step 2 — Fill service config

Option A — Stdio (recommended)

{
  "mcpServers": {
    "strength-training": {
      "command": "uvx",
      "args": [
        "--from",
        "strength-training-mcp",
        "strength-training-mcp"
      ],
      "env": {}
    }
  }
}

Option B — Streamable HTTP

Deploy the HTTP server first (see Self-Host below), then fill your public URL:

{
  "mcpServers": {
    "strength-training": {
      "type": "http",
      "url": "https://your-domain.com/mcp"
    }
  }
}

Step 3 — No parameters required

This server requires no API keys, no environment variables, and no database. Leave parameter config empty.

Step 4 — Verify

After deployment, test:

curl https://your-deployment-url/health
# → {"status":"ok","version":"0.1.1"}

Self-Host

See docs/selfhost.md for:

systemd service setup
nginx reverse proxy (hide application port)
Caddy + HTTPS
Docker deployment

Minimal production setup:

# Install
uv tool install strength-training-mcp

# Run behind nginx on port 80
strength-training-mcp-http --host 127.0.0.1 --port 3492

Then configure nginx to proxy 80 → 127.0.0.1:3492.

Tools

Tool	Purpose
`list_training_templates`	Browse the built-in program library
`get_template_plan`	Get a specific week's prescribed sessions
`lookup_exercise_form`	Get form cues + alternatives for an exercise
`explain_principle`	Explain a training science principle with citation
`calculate_fatigue_score`	Compute Banister CTL/ATL/TSB from training history
`suggest_session_modification`	Get adjustment recommendations based on fatigue + actual
`apply_plan_adjustment`	Apply aggregate adjustments to a week (deload, etc.)
`recommend_session_for_today`	Compose today's session with rationale

See docs/api.md for full tool reference, input schemas, and error codes.

Agent Integration

Claude Desktop example

{
  "mcpServers": {
    "strength-training": {
      "command": "uvx",
      "args": [
        "--from",
        "strength-training-mcp",
        "strength-training-mcp"
      ]
    }
  }
}

Development

uv sync --all-extras
uv run pytest tests/unit        # unit tests
uv run pytest tests/integration # E2E tests
uv run pytest --cov=src/strength_training_mcp

Knowledge Sources

All templates and principles cite their original public sources. See docs/rts-principles.md for citations.

License

MIT

Available Tools

8 tools

apply_plan_adjustmentB

Given a template, target week, and adjustments (DELOAD_WEEK, SCALE_WEEK, SHIFT_VOLUME, ADD_REST_DAY), return the adjusted plan JSON. The agent decides whether to persist this — the user retains veto power.

Inputs: template_id (required), week_number (required), adjustments.

calculate_fatigue_scoreA

Calculate Banister fitness-fatigue metrics (CTL/ATL/TSB) from recent training sets. Returns MISSING_BASELINE if sets is empty (constraint C4). Provide at least 14 days for partial CTL/ATL, 42 days recommended for full.

Inputs: sets (required), recovery.

explain_principleA

Explain a training science principle with source citation. Topics include: rpe_autoregulation, dup_periodization, banister_model, deload_triggers, volume_landmarks, etc. Returns body + source_citation + related_tools.

Inputs: topic (required).

get_template_planA

Get a specific week's sessions from a template. Pure function: same input returns same output. Returns a list of sessions with prescribed exercises (sets, reps, intensity, AMRAP flag).

Inputs: template_id (required), week_number (required).

list_training_templatesA

List all built-in training templates. Returns catalog of classical strength/powerlifting programs with metadata (id, name, author, weeks, days_per_week, difficulty, category, source_url).

Inputs: category, difficulty.

lookup_exercise_formA

Get form cues, common mistakes, and equipment alternatives for an exercise. Returns cues list, common_mistakes list, and alternatives list.

Inputs: exercise_name (required), equipment.

recommend_session_for_todayC

Given a template, current week, fatigue state, and last session, return today's recommended session with rationale (e.g., deload triggered).

Inputs: template_id (required), current_week (required), fatigue (required), last_session.

suggest_session_modificationB

Given a planned session, what was actually performed, and current fatigue state, return a list of suggested adjustments (scale weight, change intensity, deload, etc.). Returns {adjustments: [...], summary: '...'}.

Inputs: planned (required), fatigue (required), actual.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored 2026-06-21 13:25 across 8 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: listing templates, getting plans, adjusting plans, calculating fatigue, explaining principles, looking up exercises, recommending sessions, and suggesting modifications. There is no ambiguity between tools.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_training_templates, calculate_fatigue_score). No mixing of styles or vague verbs.

Tool Count5/5

With 8 tools, the server is well-scoped for strength training planning and education. Neither too few nor too many for the domain.

Completeness4/5

The tool set covers core operations: template listing, plan retrieval/adjustment, fatigue calculation, session recommendation/modification, and educational resources. Missing features like template creation or progress logging but these are minor gaps.

Maintenance

ActivityInactive

ResponsivenessNo recent community issues

Resources

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