Strength Training

PyPI Python MCP

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)

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
• Aura
• Cursor
• Custom Agent (Python SDK)

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