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nitinchakravarthy

Workout Tracker MCP Server

log_workout_session_to_dynamodb

Logs completed workout sessions to DynamoDB by storing session metadata and every exercise set, including weight, reps, RPE, and form ratings for tracking.

Instructions

Log a completed workout session to DynamoDB

Records the actual workout performed including session metadata and all exercise sets.
This creates:
- WorkoutLog (session metadata: start/end time, duration, energy level, etc.)
- ExerciseSetLog (one per set performed with weight, reps, RPE, form rating, etc.)

The workout_log_json should contain:
{
    "plan_id": "plan_abc",
    "week_number": 1,
    "day_number": 1,
    "workout_date": "2026-01-06",
    "started_at": "2026-01-06T14:30:00Z",
    "completed_at": "2026-01-06T15:45:00Z",
    "duration_min": 75,
    "perceived_difficulty": 8,
    "energy_level": 7,
    "sleep_quality": 8,
    "pre_workout_nutrition": "protein shake + banana",
    "bodyweight_lbs": 185,
    "status": "completed",
    "exercises": [
        {
            "exercise_id": "bench_press_barbell",
            "exercise_name": "Barbell Bench Press",
            "sets": [
                {
                    "set_number": 1,
                    "set_type": "working",
                    "weight_lbs": 225,
                    "reps_completed": 5,
                    "reps_target": 5,
                    "rpe": 8,
                    "rir": 2,
                    "tempo_actual": "2-0-1-0",
                    "rest_seconds_actual": 180,
                    "form_rating": 9,
                    "notes": "Felt strong",
                    "failed": false,
                    "spotted": false
                }
            ]
        }
    ]
}

Args:
    workout_log_json: JSON string of workout log data
    user_id: User ID who performed the workout
    log_id: Optional log ID (auto-generated UUID if not provided)
    table_name: DynamoDB table name (default: WorkoutPlans)
    region: AWS region (default: us-west-2)

Returns:
    Dictionary with success status and statistics about logged entities

Example:
    result = log_workout_session_to_dynamodb(
        workout_log_json='{"plan_id": "plan_abc", "week_number": 1, ...}',
        user_id="user_123"
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
log_idNo
regionNous-west-2
user_idYes
table_nameNoWorkoutPlans
workout_log_jsonYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states this is a write operation and enumerates the exact records created: WorkoutLog and ExerciseSetLog. It also describes the return value as a dictionary with success status and statistics. It does not cover permission requirements, idempotency, or overwrite semantics, but the side-effect disclosure is clear and substantial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long due to the necessary JSON example, but every section earns its place: purpose, created entities, payload schema, args, return value, and an example call. It is well-structured and front-loaded with the core purpose before diving into details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no output schema, and no annotations, this description is exceptionally complete. It tells the agent exactly what to pass, the structure of the JSON string, how defaults work, and what the return will look like. No critical calling information is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It does: every parameter is explained (workout_log_json, user_id, log_id, table_name, region) and the complex JSON structure is detailed with a full example including nested exercises and sets. This goes well beyond the schema's bare property names and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Log a completed workout session to DynamoDB') and the specific resource (workout session data). It further details that it creates WorkoutLog and ExerciseSetLog records, which differentiates it from sibling tools like save_workout_plan_to_dynamodb and generic log_workout.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description establishes clear context: use this tool when a workout session has been completed and needs to be persisted with session metadata and exercise sets. It doesn't explicitly name alternatives or exclusion criteria, but the detailed domain-specific behavior makes when-to-use obvious. A brief 'use save_workout_plan_to_dynamodb for plans instead' would have earned a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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