get_field_values

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 describes the core functionality well but doesn't mention important behavioral aspects like whether this is a read-only operation, potential performance implications for large datasets, or what happens with invalid field names. The description adds value by explaining the facet-building use case but misses key operational details.

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 - a clear purpose statement followed by parameter explanations. Every sentence earns its place, with no wasted words. The front-loaded purpose statement immediately communicates the tool's function, followed by essential parameter details.

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 tool with 6 parameters, no annotations, and no output schema, the description does a good job with parameters but lacks information about return format, error conditions, or performance characteristics. It's complete enough for basic understanding but misses important contextual details an agent would need for robust implementation.

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?

With 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 6 parameters. Each parameter is described with its purpose and constraints (e.g., 'Optional list of book IDs to restrict the search to', 'Optional regex to filter values', 'Max number of values to return (default 50)'), adding significant value beyond the bare 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's purpose with a specific verb ('Get') and resource ('unique values and their counts for a specific metadata field'), distinguishing it from siblings like search or content retrieval tools. It explicitly mentions the use case for 'building facets' which helps differentiate its analytical function from data-fetching siblings.

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 for when to use this tool ('Useful for building facets'), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools. It implies usage for metadata analysis rather than content retrieval, but lacks explicit exclusion guidance.

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