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Povver — Strength Training

Get Recommendations

get_recommendations
Read-only

Get pending training recommendations from TIE. Returns actionable suggestions with rationale and confidence. A hold or carry progression (type measure_progression) carries target_unit_measure (duration or distance) with target_duration_s in seconds or target_distance_m in metres, and suggested_weight in kg when the weight goes up too. Use review_recommendation to accept or reject.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
include_allNoInclude applied/rejected/expired (default: pending only)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
recommendationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real behavioral context beyond that: the shape of a hold/carry measure_progression (target_unit_measure, target_duration_s, target_distance_m, suggested_weight in kg), which an agent needs to interpret the response.

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?

Three sentences, front-loaded with purpose and followed by payload detail and the sibling handoff. The middle sentence is dense and jargon-heavy ('hold or carry progression') but every sentence carries information.

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?

An output schema exists, so return values need not be fully specified; the description nonetheless previews the important fields. Combined with a single schema-documented parameter and clear annotations, the definition is sufficient, only missing guidance on when the include_all flag is appropriate.

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

Parameters3/5

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

There is a single parameter with 100% schema description coverage, so the schema already documents include_all's meaning and default. The description adds no parameter detail, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb+resource+scope: 'Get pending training recommendations from TIE,' and describes the payload ('actionable suggestions with rationale and confidence'). It differentiates itself from the acting sibling review_recommendation, though 'TIE' is unexplained jargon.

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?

Explicitly routes the agent: 'Use review_recommendation to accept or reject,' which names the alternative and the condition that selects it. It does not, however, state when to use include_all=true versus the pending-only default, leaving that to the schema.

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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