trail-factors

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matched_terrain_drift

Computes heart-rate drift on same-ground runs by pairing speed-matched records at least 10 minutes apart, offering a fallback when steady-effort gating fails.

Instructions

§6.7 HR drift on the SAME ground, bpm/hour, over a selection of runs.

Pairs RECORDS within 20 m of each other, at least 10 min apart, BOTH
rwd_run, with speed matched to 0.35 m/s. Walking is not paired. Read
`applied_filters` — those tolerances are what the number means.

Effort-matched by construction, so it is the substitute when §6.4's
steady-effort gate fails. Read `excluded` too — a drift figure over "five
long runs" means something different if two were dropped for coverage.

`segment_effort_drift` is a DIFFERENT quantity, usually several times
larger. Neither is "the" drift.

Input Schema

Name	Required	Description	Default
`last_n`	No
`cluster`	No
`end_date`	No
`run_type`	No
`direction`	No
`segment_id`	No
`start_date`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 2, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden well: it discloses pairing rules, distance and time tolerances, speed matching, exclusion of walking, and that users should inspect `applied_filters` and `excluded`. It does not state read-only or permission assumptions, but the operational filtering behavior is unusually well documented.

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 the metric definition and remains relatively tight. It includes some internal section references and backtick field names that may be unnecessary noise, but every substantive sentence adds behavioral or selection context.

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?

The output schema means return values need not be explained, and the description explains the metric's construction and exclusions well. However, for a tool with seven undocumented input parameters and no annotations, the description is materially incomplete for correct invocation because it provides no parameter guidance.

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

Parameters1/5

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

The schema has seven parameters with 0% description coverage, and the description does not explain any of them. Terms like `last_n`, `cluster`, `run_type`, `direction`, and `segment_id` are left entirely for the agent to infer, so the description fails to compensate for the schema gap.

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 a specific metric and scope: HR drift on the same ground, expressed in bpm/hour, over selected runs. It explicitly distinguishes this quantity from `segment_effort_drift`, so an agent can tell which drift metric is being requested.

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?

It gives a clear usage condition: this is the substitute when the steady-effort gate in §6.4 fails. It also warns that `segment_effort_drift` is a different quantity and that neither is 'the' drift, which directly helps tool selection. The only weakness is reliance on an external section reference.

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