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r-huijts

Strava MCP Server

by r-huijts

get-activity-laps

Retrieve detailed lap data from Strava activities for performance analysis. Access timestamps, speeds, distances, and metrics to track and compare athletic performance.

Instructions

Retrieves detailed lap data for a specific Strava activity.

Use Cases:

  • Get complete lap data including timestamps, speeds, and metrics

  • Access raw values for detailed analysis or visualization

  • Extract specific lap metrics for comparison or tracking

Parameters:

  • id (required): The unique identifier of the Strava activity.

Output Format: Returns both a human-readable summary and complete JSON data for each lap, including:

  1. A text summary with formatted metrics

  2. Raw lap data containing all fields from the Strava API:

    • Unique lap ID and indices

    • Timestamps (start_date, start_date_local)

    • Distance and timing metrics

    • Speed metrics (average and max)

    • Performance metrics (heart rate, cadence, power if available)

    • Elevation data

    • Resource state information

    • Activity and athlete references

Notes:

  • Requires activity:read scope for public/followers activities, activity:read_all for private activities

  • Returns complete data as received from Strava API without omissions

  • All numeric values are preserved in their original precision

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe identifier of the activity to fetch laps for.
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing important behavioral traits: authentication requirements ('Requires activity:read scope...'), data completeness ('Returns complete data... without omissions'), and precision handling ('All numeric values are preserved...'). It doesn't mention rate limits or error conditions, keeping it from a perfect score.

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?

Excellent structure with clear sections (Description, Use Cases, Parameters, Output Format, Notes). Every sentence earns its place by adding specific value - no redundant information. The description is appropriately sized and front-loaded with the core purpose.

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?

For a single-parameter read operation with no output schema, the description provides exceptional completeness. It covers authentication requirements, data scope, output format details (both human-readable and JSON), and specific data fields returned. This gives the agent sufficient context to use the tool effectively.

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?

Schema description coverage is 100%, so the schema already documents the single 'id' parameter adequately. The description adds minimal value beyond the schema by specifying it's for 'a specific Strava activity' and listing it in the Parameters section, but doesn't provide additional syntax or format details.

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 clearly states the tool's purpose with specific verb ('Retrieves') and resource ('detailed lap data for a specific Strava activity'). It distinguishes from siblings like 'get-activity-details' by focusing exclusively on lap data rather than general activity information.

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 'Use Cases' section provides clear context for when to use this tool (detailed lap analysis, visualization, comparison). However, it doesn't explicitly state when NOT to use it or name specific alternatives among sibling tools, though the focus on lap data implies differentiation from general activity tools.

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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