stop_execution

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

No annotations are present, so the description carries the disclosure burden. It discloses the core effects (stop timing, mark complete, record timestamp, calculate duration) but does not mention prerequisites, idempotency, or side effects on execution status. Adequate but with notable gaps.

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?

Two compact sentences with the primary action front-loaded. No filler or redundant information; every sentence earns its place.

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 the tool's simplicity (no params, output schema present), the description is largely complete. It explains what and when, though it could explicitly tie to start_execution or restore_execution for lifecycle clarity.

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 tool has zero parameters, so the schema fully covers parameters. Per the baseline for no-parameter tools, this is a 4; the description adds no conflicting parameter information.

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?

The description clearly states it stops timing and marks execution complete, with specific details about recording the stop timestamp and calculating duration. This distinguishes it from sibling tools like start_execution and update_execution_status through the timing-specific language.

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?

Directly instructs to call after the ML workflow completes, providing a clear when-to-use context. It does not mention alternatives or exclusions, but the guidance is sufficient for a paired lifecycle tool.

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