list_explained_samples

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It helpfully clarifies this is a 'discovery tool' that doesn't perform computation, which is valuable behavioral context. However, it doesn't mention other important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what format the summary returns.

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 perfectly structured and concise. It starts with the core purpose, explains what it returns, clarifies what it doesn't do, and then provides parameter semantics. Every sentence earns its place with no wasted words, and the information is front-loaded appropriately.

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?

For a discovery/list tool with 2 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the purpose and parameter meanings well, but lacks information about the return format, pagination, error conditions, or authentication requirements that would be helpful for an AI agent to use this tool effectively.

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 schema description coverage is 0%, so the description must compensate. It provides meaningful context for both parameters: 'model_id' as 'Model identifier' and 'run_id' as 'Filter to a specific batch run'. This adds valuable semantic understanding beyond the bare schema, though it doesn't specify format examples or constraints for these identifiers.

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's purpose with specific verbs ('browse', 'returns a summary') and resources ('samples with precomputed explanations', 'result store'). It explicitly distinguishes itself from computation tools by stating 'it does NOT compute explanations on the fly', which differentiates it from sibling tools like explain_prediction or explain_prediction_waterfall.

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 clear context about when to use this tool: for discovery of precomputed explanations rather than on-the-fly computation. However, it doesn't explicitly mention when NOT to use it or name specific alternative tools from the sibling list, though the distinction from computation tools is implied.

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