close_experiment

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

Annotations declare idempotentHint=true and destructiveHint=false. The description adds behavioral detail: never commits for you, returns a suggestion, and that verdict is computed. This goes beyond annotations without contradiction.

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 three sentences, front-loaded with the key verb 'Finalize', and every sentence adds distinct information. No wasted words.

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 complexity (computation, propagation, verdict) and presence of output schema, the description covers core logic. However, it does not explain verdict conditions or output format details, leaving some gaps.

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?

With 0% schema description coverage, the description should explain parameters, but it only indirectly refers to experiment_id (via context) and ignores the 'notes' parameter. No parameter-level detail is provided.

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 specifies the action ('Finalize'), the computed outputs (observed_effects, prediction_match, verdict), and the propagation to hypothesis. It distinguishes from sibling tools like start_experiment and propose_hypothesis by detailing unique behavior.

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?

The description provides usage context (e.g., 'you cannot assert confirmed', verdict is computed) and hints at workflow (e.g., returns commit suggestion). However, it lacks explicit when-not-to-use instructions or direct alternatives from the sibling list.

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