strength-training-mcp
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.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@strength-training-mcpRecommend 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 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 | ✅ |
| Claude Desktop, Claude Code, Cursor |
Streamable HTTP | ✅ |
| ModelScope, Aura, remote agents |
SSE | ❌ | — | Not implemented (use Streamable HTTP instead) |
Related MCP server: nutrition-mcp
Quick Start
Stdio (local clients)
uvx --from strength-training-mcp strength-training-mcpHTTP server (remote / cloud)
uvx --from strength-training-mcp strength-training-mcp-http --port 8080Test:
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 3492Then configure nginx to proxy 80 → 127.0.0.1:3492.
Tools
Tool | Purpose |
| Browse the built-in program library |
| Get a specific week's prescribed sessions |
| Get form cues + alternatives for an exercise |
| Explain a training science principle with citation |
| Compute Banister CTL/ATL/TSB from training history |
| Get adjustment recommendations based on fatigue + actual |
| Apply aggregate adjustments to a week (deload, etc.) |
| 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_mcpKnowledge Sources
All templates and principles cite their original public sources. See docs/rts-principles.md for citations.
License
MIT
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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