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get_scheduled_workout_summaries

Read-only

Fetch every workout scheduled in a date range, including the IDs needed to unschedule them, and distinguish Garmin Coach plans from self-scheduled entries.

Instructions

POSTs the fixed GraphQL query workoutScheduleSummariesScalar(startDate, endDate): every scheduled workout in the range, plan or self-scheduled (tpType: null), as compact rows whose scheduledWorkoutId is what unscheduleWorkout takes, or null for a Garmin Coach workout, which also has tpType: null like a self-scheduled one (tell them apart by fbtAdaptivePlanId). Dates are validated before they are spliced into the query. A GraphQL error arrives as HTTP 200 with errors, turned into a GarminError. It LAGS writes: a just-scheduled workout is in the month feed at once but abse…

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
enddateYesCalendar date, YYYY-MM-DD (UTC)
startdateYesCalendar date, YYYY-MM-DD (UTC)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior5/5

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

Goes well past the readOnlyHint/openWorldHint annotations: it discloses the transport (POST of a fixed GraphQL query), that dates are validated before splicing, that GraphQL errors arrive as HTTP 200 with an `errors` payload converted to GarminError, and that the feed lags writes. These are exactly the operational traits an agent needs to interpret failures and staleness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single dense run-on sentence studded with backticked identifiers, and it opens on the implementation detail ('POSTs the fixed GraphQL query...') rather than the user-facing purpose. The clipped trailing 'abse…' also leaves the important lag sentence unfinished.

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 no output schema, the description must explain the return value, and it does: compact rows keyed by scheduledWorkoutId, with null-id Garmin Coach entries disambiguated via fbtAdaptivePlanId. The only shortfall is the truncated lag statement and the absence of any note on volume/pagination.

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 100% and both date parameters are already documented as YYYY-MM-DD UTC. The description adds that the dates are validated and spliced into the query, but no additional syntax, range limits, or semantics beyond the schema.

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?

States a specific resource and scope: 'every scheduled workout in the range, plan or self-scheduled', returning 'compact rows'. It details the distinguishing fields (scheduledWorkoutId, tpType, fbtAdaptivePlanId) so an agent knows what comes back, but never names the sibling tools (get_scheduled_workouts, get_scheduled_workout_by_id, get_next_scheduled_workout) it competes with.

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?

Usage is only implied through the date-range scope and the 'It LAGS writes' caveat, which does tell the agent when this feed may be stale. There is no explicit when-to-use versus the other scheduled-workout getters, so the agent must infer the choice from the return shape.

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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