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hhopke

intervals-icu-mcp

by hhopke
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Icu Get Pace Curves

icu_get_pace_curves
Read-onlyIdempotent

Fetch best sustained pace across durations from 5 seconds to 1 hour over a chosen window for run or swim fitness trends and race-time predictions, with optional Grade-Adjusted Pace for hills.

Instructions

Fetch the pace-vs-duration curve — best (fastest) sustained pace across durations from 5s up to 1h, aggregated over the chosen window.

Use for run/swim fitness trends and race-time predictions. Pass use_gap=True to normalize for hills via Grade-Adjusted Pace. For time-in-zone distribution within a single activity, use get_pace_histogram (or get_gap_histogram) instead.

Input Schema

NameRequiredDescriptionDefault
use_gapNoUse Grade Adjusted Pace (GAP) for running
days_backNoNumber of days to analyze (optional)
athlete_idNoAthlete ID (for coaches managing multiple athletes)
sport_typeNoSport type (e.g., Run, Swim)Run
time_periodNoTime period shorthand: 'week', 'month', 'year', 'all' (optional)

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv5.0.1
    • addedInput schema / properties / athlete_id
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Athlete ID (for coaches managing multiple athletes)"
+}
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent and non-destructive, so the description is free to spend its words on substance: the duration range covered, the aggregation window, and that use_gap applies Grade-Adjusted Pace normalization for hills. It does not discuss athlete scoping or whether results are per-activity or cross-activity beyond 'aggregated over the chosen window'.

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?

Three compact sentences, front-loaded with what the tool returns, then the use case, then the sibling redirect. No restatement of the name and no filler.

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 described, and all five parameters are documented in the schema. The only gap is the absence of guidance on athlete_id scoping for coaches and on precedence between days_back and time_period, which matters for a windowed aggregation tool.

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 coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it explains that use_gap normalizes for hills (the schema only names GAP) and that results are aggregated over the chosen window, which ties days_back and time_period together. It does not clarify how days_back and time_period interact when both are supplied.

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?

States a specific verb and resource ('Fetch the pace-vs-duration curve') and immediately pins down the semantics: best sustained pace across 5s to 1h, aggregated over the chosen window. That is enough to separate it from icu_get_pace_histogram and other curve tools without opening a schema.

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?

Gives a positive use case ('run/swim fitness trends and race-time predictions') and an explicit exclusion with the named alternative: use get_pace_histogram or get_gap_histogram for time-in-zone distribution within a single activity. Both when-to-use and when-not-to-use are present.

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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