strength-training-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| 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). |
| 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). |
| 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: '...'}. |
| 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. |
| 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). |
| 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. |
| lookup_exercise_formA | Get form cues, common mistakes, and equipment alternatives for an exercise. Returns cues list, common_mistakes list, and alternatives list. |
| 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/daiduo2/strength-training-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server