label_find

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

With no annotations, the description carries the burden of disclosure. It reveals that matching is case-insensitive and substring-based, which is useful. However, it does not describe what the tool returns (matching labels, IDs, positions), what happens when no labels match, or whether search covers both point and region labels.

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 compact and well-organized: core purpose first, then a practical use case, then parameter documentation. No redundant or filler sentences.

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 one-parameter search tool, the description covers the main action and parameter well. However, with no output schema, it omits return value details and expected output format. Given the simple scope and sibling context, this is a moderate gap rather than a critical one.

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 coverage is 0%, so the description must compensate for the bare 'Query' title. It does: 'query: Text to search for within label text' clearly explains the parameter's meaning and scope. A minor example or edge-case note would push it higher, but the single parameter is adequately explained.

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 states a specific action: 'Find labels whose text contains a search string (case-insensitive).' This clearly names the resource (labels), the operation (find by text), and a distinguishing behavior (case-insensitive substring matching).

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 gives clear context for when this is useful: 'locating a specific marker in a long recording without reading through every label.' It does not explicitly contrast with alternatives like label_list or label_get_all, but the use case is clear enough to guide selection.

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