list_experiments

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

No annotations are present, so the description should disclose read-only behavior or any side effects. It does not mention that the tool is read-only, nor does it describe pagination, ordering, or performance implications.

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 a single sentence with no wasted words. However, it is overly terse and omits important details about parameters and behavior, which reduces its effectiveness.

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 simplicity of the tool (1 parameter, no output schema), the description should still clarify the output structure and parameter usage. It mentions 'latest metric + validator verdict' but does not explain the list format or how 'status' filters results. Comparison with sibling tools is lacking.

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 0% – the 'status' parameter is not mentioned in the description at all. The parameter is optional with a default empty string, but its purpose and allowed values are unexplained.

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 verb 'list' and the resource 'experiments', and specifies the included data (latest metric + validator verdict). This distinguishes it from single-experiment tools like 'get_experiment' but not from similar list tools like 'get_overview' or 'list_ideas'.

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?

No guidance on when to use this tool versus alternatives such as 'get_experiment_results' or 'get_overview'. No exclusions or context are provided.

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