trail-factors

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resistance_history

Track heart rate at matched resistance over calendar time, treating each exposure as one block and separating blocks without stated RPM; groups with fewer than three blocks report insufficient trend.

Instructions

§18.1 HR at matched resistance over CALENDAR TIME — the longitudinal cut.

EFFECTIVE n IS THE NUMBER OF BLOCKS, NEVER THE NUMBER OF HR RECORDS. A
block is one exposure to one resistance; its several hundred HR samples are
one observation of it. This project has made the opposite mistake twice
(§6.9, §2.6) and both are recorded as the same disease.

A group with fewer than three blocks reports `trend_status: insufficient`
and says how many it has, rather than drawing a line through two points.
Blocks with no stated RPM are grouped SEPARATELY from blocks that have one:
no stated cadence is not a cadence that happens to match.

Input Schema

Name	Required	Description	Default
`match_rpm`	No
`resistance`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 2, 2026

TDQS

B3/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose key behavior: effective n is the number of blocks, fewer than three blocks returns trend_status insufficient, and missing RPM is grouped separately. It does not cover auth/rate-limit behavior, but for an analysis tool the important statistical semantics are present.

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

Conciseness3/5

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

The core purpose is front-loaded, but the project-history aside with section references (§6.9, §2.6) and 'same disease' does not help an agent select or call the tool. The all-caps emphasis and internal references add noise.

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

Completeness2/5

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

An output schema exists, so return shape need not be explained. However, with no annotations and 0% schema description coverage, the description leaves both parameters and usage relative to siblings unexplained, which is a substantial gap for a two-parameter analysis tool.

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

Parameters2/5

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

Schema coverage is 0% for both parameters, and the description never defines what match_rpm controls or how the resistance value is interpreted. It mentions matched resistance and RPM grouping, but does not explain the boolean toggle or default resistance=0.

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?

The opening states a specific analysis: HR at matched resistance over calendar time, described as the longitudinal cut. This is clear enough to distinguish it from aggregate/resistance tools, but it never names a sibling or explicitly says it returns a time series.

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

Usage Guidelines2/5

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

The description explains internal grouping behavior, but gives no guidance on when to use this tool versus time_in_resistance, ride_blocks, or other siblings. There are no stated exclusions or selection conditions.

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