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Delete exercise entry

fitness_delete_exercise
Destructive

Delete logged exercise entries by case-insensitive name match, optionally for a specific date or all matching rows.

Instructions

Remove exercise-diary entries by name match (case-insensitive).

query: text matched against logged exercise names. By default deletes exactly one entry, like fitness_delete_food: an exact name match wins; otherwise several matches raise an error listing the candidates, and no match raises an error listing what's actually logged. Of several identical rows (same name and columns), one is removed. all_matches: delete every entry whose name contains query — for cleaning up syncs that log one workout as several same-named rows (e.g. Garmin's "Aerobics, general"). Check fitness_get_exercise_entries first; a short query can match many entries. date: YYYY-MM-DD (default: today).

Input Schema

NameRequiredDescriptionDefault
dateNo
queryYes
all_matchesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.3

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already flag destructiveHint=true and non-idempotency, and the description goes well beyond them: default single-row deletion, exact-match precedence, error behavior when several or zero matches occur, which identical row is removed, and the warning that a short query can wipe many entries. That is exactly the blast-radius context an agent needs before a destructive call.

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?

Purpose is front-loaded and every subsequent sentence (error cases, all_matches semantics, date format) adds operational value. It is longer than average, but the density is justified; only the 'like fitness_delete_food' aside could be trimmed without loss.

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?

With no output schema, the description still covers default vs. bulk behavior, ambiguity/zero-match error outcomes, and per-parameter formats. An agent has everything required to call it correctly and anticipate the consequences.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries the load entirely — and it does: query is a case-insensitive substring/exact match against logged names, all_matches switches from one-entry to all-contains deletion, and date is documented with its YYYY-MM-DD format and today-default. All three parameters gain meaning beyond the bare schema.

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 opens with a precise verb+resource ('Remove exercise-diary entries') and adds the matching mechanism (case-insensitive name match). It is clearly distinguishable from the sibling food-deletion tools and from the read-only fitness_get_exercise_entries.

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?

It gives concrete context for the all_matches flag ('for cleaning up syncs that log one workout as several same-named rows') and directs the agent to check fitness_get_exercise_entries first. It stops short of an explicit exclusion statement (e.g. use fitness_delete_food for food entries), so it is strong context rather than full when/when-not guidance.

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