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get_ftp_history

Retrieve historical Functional Threshold Power (FTP) for a specific sport over a date range, with daily, weekly, monthly, or yearly aggregation.

Instructions

Get historic Functional Threshold Power for a sport over a date range.

Args: start_date: First date in the range, as YYYY-MM-DD. end_date: Last date in the range, as YYYY-MM-DD. sport: Garmin sport key, e.g. "RUNNING", "CYCLING". aggregation: One of "daily", "weekly", "monthly", "yearly".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sportNoCYCLING
end_dateYes
start_dateYes
aggregationNodaily

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden. It communicates a read-style operation and date constraints, but does not explain what the response contains, how aggregation affects results, timezone handling, or data availability. For a tool with no output schema, this is a notable gap.

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?

A single focused summary line is followed by a compact Args block with no filler. Every sentence contributes either the core purpose or a parameter detail, and the most important information is front-loaded.

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

Completeness3/5

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

For a simple retrieval tool with four parameters, the description adequately defines inputs and purpose. However, it does not describe the return shape or how aggregation alters the results, and with no output schema or annotations this leaves the agent with moderate uncertainty about what to expect.

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?

The input schema has 0% description coverage, but the Args block fully compensates by documenting all four parameters: date format for start_date and end_date, an example Garmin sport key for sport, and explicit allowed values for aggregation. This adds substantial meaning beyond the bare schema.

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?

Description opens with a specific verb 'Get' and a clear resource: historic Functional Threshold Power for a sport over a date range. It does not explicitly contrast itself with sibling get_cycling_ftp, but the word 'historic' and the date-range qualifier strongly distinguish it from a current-value query.

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

Usage Guidelines3/5

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

The purpose implies this tool is for historical FTP queries over date ranges, and the sibling name get_cycling_ftp hints at an alternative for current values. However, the description gives no explicit when-to-use guidance, exclusions, or named alternatives, so an agent must infer the correct selection.

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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