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get_activity_streams

Read-only

Fetch per-second Strava activity streams (heart rate, power, cadence, altitude, speed, position) downsampled to aligned points for pacing, heart-rate drift, and interval analysis.

Instructions

Second-by-second data recorded during one activity (heart rate, power, cadence, altitude, speed in m/s, position), evenly downsampled so every stream lines up point for point. Use it for pacing, heart-rate drift, or interval analysis. Each stream reports its original_size.

Input Schema

NameRequiredDescriptionDefault
keysNoStreams to fetch, any of: time, distance, latlng, altitude, velocity_smooth, heartrate, cadence, watts, temp, moving, grade_smooth. Defaults to time, distance, altitude, heartrate, cadence, watts and velocity_smooth.
max_pointsNoPoints kept per stream after even downsampling, 2 to 10000.
activity_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds genuinely non-obvious behavior: streams are evenly downsampled so they align point-for-point, and each stream reports its original_size, which tells the agent to expect truncated data and how to detect it. It omits nothing critical, though rate/limits and default key set are left to the schema.

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?

Three tight sentences: what the data is, what to use it for, and the original_size caveat, all front-loaded with no filler. Slightly could be trimmed since the stream list overlaps the schema.

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?

With an output schema present, return values need not be explained, and the description covers the sampling contract and the original_size field that an agent needs to interpret truncated results. The only gap is the absence of routing guidance relative to nearby activity tools.

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 coverage is 67% and the keys parameter already enumerates all valid streams, so the description's mention of HR/power/cadence/etc. largely restates the schema. It does add meaning for max_points by framing the downsampling contract ('point for point'), but activity_id remains undocumented in both places. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource and content: second-by-second streams (HR, power, cadence, altitude, speed, position) for one activity, plus the downsampling guarantee. That is far more informative than the bare name and implicitly separates it from summary tools like get_activity and get_activity_laps. It stops short of naming a sibling or stating the boundary explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use it for pacing, heart-rate drift, or interval analysis' gives concrete analytical contexts, so usage is more than implied. However, there is no guidance on when NOT to use it or which sibling to prefer (e.g., get_activity_laps for lap-level splits, get_activity for summary metrics), leaving tool selection to inference.

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