Get Experiment Results

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral context by detailing what results are shown (sample counts, metric comparisons, significance, winner), which goes beyond annotations and helps the agent understand output.

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 sentences: first states purpose with specific outputs, second provides an exclusion with an alternative. Every sentence adds value with no waste.

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?

The description fails to clarify how to specify which experiment to get results for (no experiment_id in schema). It assumes context that might not be obvious, leaving a significant gap in completeness for a tool that requires identifying an experiment.

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?

Schema description coverage is 100%, with each parameter having a clear description (filter by agent, filter by status). The description adds no extra meaning beyond the schema, so it meets the baseline of 3.

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 the tool gets current results of a model routing experiment, listing specific outputs like sample counts and metric comparisons. It explicitly distinguishes from the sibling tool 'create_model_experiment' by saying 'Do NOT use for starting experiments'.

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 explicitly says when not to use the tool (starting experiments) and directs to the correct sibling tool 'create_model_experiment'. This provides clear guidance for the agent.

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