Hevy MCP Pro

get_volume_summary

Calculate total training volume (weight*reps) from recent Hevy workouts, grouped by exercise, muscle group, or week. Filter by date to analyze training load over any period.

Instructions

Total training volume (weight_kg * reps, nulls => 0) grouped by exercise, muscle_group, or week. Paginates recent workouts since since (ISO 8601, default 90 days ago). May include a warning field if results were truncated.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_volume_summary"
  }
}

Input Schema

Name	Required	Description	Default
`since`	No
`groupBy`	No		exercise

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 10, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It reveals important traits: nulls are treated as 0 in calculations, pagination occurs over recent workouts, a default 90-day window exists, and a 'warning' field may indicate truncation. This goes beyond the schema and gives the agent actionable expectations.

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 two sentences and front-loaded with the primary function. It immediately states what the tool does, then adds necessary details (null handling, date range, pagination, warning) without extraneous text. Every sentence earns its place.

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

Completeness4/5

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

For a tool with no annotations and no output schema, the description covers the core functionality, parameter semantics, pagination, and truncation behavior. However, it does not specify whether results are limited to the current user's workouts or how pagination works (e.g., page size or cursor), leaving some ambiguity. Still, it is fairly complete for an aggregation tool.

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

Parameters4/5

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

Schema coverage is 0%, but the description compensates by explaining both parameters: 'since' is ISO 8601 with a default of 90 days ago, and the grouping dimensions (exercise, muscle_group, week) map directly to the 'groupBy' enum. It also clarifies the volume formula, adding meaning beyond the raw schema.

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 tool computes total training volume (weight_kg * reps) with a precise formula, and distinguishes it from sibling tools by emphasizing aggregation/grouping rather than listing workouts. It names valid grouping dimensions, making the purpose unambiguous.

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 provides clear context for when to use this tool: to get aggregated volume grouped by exercise, muscle_group, or week, within a date range. It doesn't explicitly exclude alternatives, but the purpose is sufficiently specific that an agent can infer it's for summary/analysis rather than raw workout lists.

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