get_workout

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

With no annotations, the description carries the full burden. It declares read-only behavior, clearly states the error condition, and lists the types of data returned. This is sufficient for an agent to understand the tool's behavior without surprises.

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

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

The description is concise (3 sentences), front-loaded with the main purpose, and every sentence adds value. No fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains return content (laps, HR zones, training-effect, sport-specific), mentions error handling, and references related tools. It is complete for a simple retrieval tool.

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

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

The input schema has 100% coverage, but the description adds significant context: the opaque nature of workoutKey, requirement to discover via list_workouts, and the specific error on invalid key. This goes beyond the schema's own description.

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

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

Description clearly states it returns the full summary for one workout, listing specific fields (laps, HR zones, training-effect, sport-specific metrics). It distinguishes from siblings like get_workout_samples and list_workouts by specifying what this tool does versus alternatives.

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

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

Explicitly tells the agent to use list_workouts to discover valid workoutKey values and directs to get_workout_samples for time-series data. Also warns that invalid key throws SuuntoNotFoundError, providing clear when-to-use and when-not-to.

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