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get_activities

Read-onlyIdempotent

Get completed workout sessions. detail='summary' (preferred for browsing) returns name, date, duration, and exercise names per activity; detail='full' adds every set performed vs target. For a single exercise's progress over time, prefer get_exercise_history instead.

Args: start_date: Start of date range (ISO format YYYY-MM-DD). Defaults to 30 days ago. end_date: End of date range (ISO format YYYY-MM-DD). Defaults to today. workout_name: Optional filter to only show activities for a specific workout. limit: Max results to return (default 10, max 100). detail: 'summary' or 'full' (default 'full').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10, max 100)
detailNo'summary' = names/dates only; 'full' = per-set detail (default)
end_dateNoEnd of date range (YYYY-MM-DD). Defaults to today.
start_dateNoStart of date range (YYYY-MM-DD). Defaults to 30 days ago.
workout_nameNoOptional filter by workout name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
activitiesYes

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description goes beyond them by clarifying that only completed workout sessions are returned and that 'full' includes sets performed vs target. No contradictions or hidden side effects are hinted at.

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

Conciseness4/5

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

The description is front-loaded with behavior and usage, then lists parameters with defaults. It is slightly redundant with the schema's parameter descriptions, but every sentence contributes either semantic context or invocation guidance, so it remains appropriately sized.

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?

With a complete input schema, output schema, and strong annotations, the description fills the remaining gaps: the completed-session scope, the detail-mode tradeoff, default date range, filters, and the get_exercise_history alternative. Nothing essential for correct invocation is missing.

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 description coverage is 100%, so baseline is 3, but the description adds meaning by explaining what each detail mode returns and marking summary as preferred for browsing. It also reiterates defaults clearly, adding modest value over the schema alone.

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 states it "Get[s] completed workout sessions" and elaborates the two detail modes, giving a concrete verb, resource, and scope. It also distinguishes itself from get_exercise_history by directing single-exercise progress queries there, so an agent can tell tools apart.

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

Usage Guidelines5/5

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

It explicitly advises that detail='summary' is preferred for browsing and that get_exercise_history should be used "for a single exercise's progress over time." This gives both a recommended invocation style and a clear alternative condition.

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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