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MCPacer

MCPacer

by wernerpe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_run_detail

Retrieve detailed run data including lap splits, heart rate, elevation, and coach notes for post-race analysis or investigating specific splits.

Instructions

    Get a detailed view of a single run — full lap splits, HR, elevation,
    digest, and coach notes. Use for post-race analysis, anomalies, or
    when the athlete asks about specific splits.

    Args:
        activity_id: The Strava activity ID.

    Returns:
        Formatted run detail as text.

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": {
      "activity_id": "<activity_id>"
    },
    "name": "get_run_detail"
  }
}

Input Schema

NameRequiredDescriptionDefault
activity_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations provided, so description carries burden. Discloses return format as 'Formatted run detail as text.' Does not mention authentication, rate limits, or side effects, but likely read-only.

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?

Very concise with a clear header, use case statement, and structured Args/Returns sections. No wasted words.

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

Completeness5/5

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

Has output schema, so return values are covered. Describes input, output format, and usage context. For a simple tool, this is complete.

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_description_coverage is 0% (no schema descriptions), but the description adds meaning: 'The Strava activity ID.' Single parameter clearly explained, compensating for 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?

Clearly states 'Get a detailed view of a single run' with specific data (lap splits, HR, elevation, digest, coach notes). Differentiates from siblings by emphasizing detailed post-race analysis.

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?

Provides explicit use cases: 'post-race analysis, anomalies, or when the athlete asks about specific splits.' Lacks explicit exclusion criteria or alternatives, but context is clear.

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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