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anastasiakrause

Weekly Planning Assistant MCP

by anastasiakrause
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Python
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suggest_workout_slots

Finds available calendar gaps and suggests ranked workout time slots based on preferred times, duration needs, and commute patterns.

Instructions

Find optimal times for booking fitness classes.

Suggests time slots based on:
- Availability (free time in calendar)
- Preferred time of day
- Duration requirements
- Commute patterns (avoids early morning before office days)

Args:
    start_date: Start date (YYYY-MM-DD)
    end_date: End date (YYYY-MM-DD)
    preferred_times: Preferred time categories. Defaults to config setting.
    min_duration_minutes: Minimum duration needed. Defaults to config setting.
    calendar_ids: List of calendar IDs. Defaults to ["primary"].

Returns:
    List of suggested workout slots ranked by quality score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateYes
start_dateYes
calendar_idsNo
preferred_timesNo
min_duration_minutesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does disclose real traits: the ranking heuristic (availability, preferred time of day, duration, commute patterns avoiding early mornings before office days) and that omitted parameters fall back to config settings. It does not state read-only/non-mutating behavior explicitly or mention auth or rate limits, so it stops short of full disclosure.

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?

Front-loads the purpose, then uses tight Args/Returns sections with no filler. Slightly list-heavy, and the Returns line restates what the output schema already conveys, but overall efficient and scannable.

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 5-parameter read-style recommendation tool with an output schema present, the description covers inputs, defaults, and the ranking behavior well enough to call it correctly. The only omissions are mutation/side-effect clarity and the enum values for preferred_times.

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%, so the description must compensate, and its Args block documents all five parameters including date format (YYYY-MM-DD), the meaning of preferred_times, and the config-backed defaults for the three optional parameters. It doesn't enumerate the morning/lunch/evening enum values, but that gap is minor against the otherwise complete coverage.

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 opens with a specific verb+resource: 'Find optimal times for booking fitness classes,' and the name suggest_workout_slots matches. An agent can tell this is a slot-recommendation tool apart from analyze_week or find_danger_zones, though it never explicitly names or contrasts those siblings.

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 implied by the framing ('for booking fitness classes') and the enumerated ranking factors, but there is no explicit statement of when to call this instead of analyze_week or check_commute_requirements, and no prerequisites or exclusions. Minimum-viable guidance only.

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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