sw_list_results

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses the optional filter behavior (if dataset_id is not given, list all), which is useful. However, it does not mention read-only nature, pagination, or any other side effects, making it only moderately transparent.

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 concise sentence that front-loads the purpose and behavior. It contains no filler and is appropriately sized for the tool's simplicity.

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 simple listing tool with one optional parameter and an output schema, the description is fairly complete. It covers the primary behavior and the parameter's effect. The presence of an output schema reduces the need to explain return formats. Minor gap: no mention of sorting or limits, but not critical for this tool.

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 has one parameter (dataset_id) with no description, and schema description coverage is 0%. The description compensates by explaining that dataset_id is a filter and omitting it lists all results, adding meaningful semantics beyond the raw schema.

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 lists registered external algorithm results, using a specific verb ('列出') and resource ('外部算法结果'). This distinguishes it from sibling tools like sw_list_presets and sw_list_scenes.

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 minimal usage guidance: it explains that omitting dataset_id lists all results. However, it does not mention when to use this tool vs alternatives like sw_compare_results or any exclusions, leaving the agent without clear decision support.

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