TrackLab MCP Ultimate Edition

predict_race

Predict race finish times from a known distance and time using Riegel and Daniels models, with validity checks for reliable results.

Instructions

Predicts race finish time using Riegel & Daniels models with validity checking.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "knownDistanceMeters": "<knownDistanceMeters>",
      "knownTimeSeconds": "<knownTimeSeconds>",
      "targetDistanceMeters": "<targetDistanceMeters>"
    },
    "name": "predict_race"
  }
}

Input Schema

Name	Required	Description
`knownTimeSeconds`	Yes	Sample finish time in seconds (e.g. 1200)
`knownDistanceMeters`	Yes	Sample race distance in meters (e.g. 5000)
`targetDistanceMeters`	Yes	Target race distance in meters (e.g. 42195)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 7, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It adds 'validity checking,' which suggests input validation behavior beyond a simple calculation, but it does not detail what validity means, what constraints apply, or how invalid inputs are handled.

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 sentence, front-loaded with the action and resource, and every phrase contributes meaning. No filler or repetition.

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?

For a simple three-parameter computation with full schema coverage, the description provides the essential information: what it does and the modeling approach. It would benefit from stating the return value or caveats, but it is not inadequate.

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?

The input schema already covers 100% of the parameters with clear descriptions and examples. The description's mention of Riegel & Daniels models and validity checking is useful context but adds no parameter-specific semantics beyond the schema.

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 opens with a specific verb ('Predicts') and a clear resource ('race finish time'), and distinguishes this tool from siblings by naming the Riegel & Daniels models. This leaves no ambiguity about the tool's core function.

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?

The description clearly implies the use case: predicting finish times from a known race performance. However, it does not explicitly name alternatives like calculate_vdot or generate_race_plan, nor state when not to use it, so it lacks explicit exclusions.

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