get_facets

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

Annotations are absent, so the description carries the behavioral burden. The verb 'get' implies a read-only operation and the description states it returns counts, but it does not disclose pagination, privacy scoping, or any side effects. It gives useful contextual positioning but stops short of a full behavioral profile.

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 two sentences with no fluff. The functional purpose is front-loaded, followed by a succinct, non-repetitive usage directive. Every word earns its place.

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 tool's low complexity—three parameters, one required, no nested objects, and no output schema—the description is largely sufficient. It conveys the tool's purpose, return content, and the immediate usage context, leaving only peripheral details like error behavior or pagination unexplained.

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 100%, so the baseline is 3. The description mentions 'where' and 'refine' filters only to explain the return value's purpose; it does not add new semantics or formats for those parameters beyond what the schema already provides.

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 uses a specific verb-resource pair ('Get facet values and their record counts') and confines the scope to a dataset's filterable fields. It also distinguishes the tool from query_records by saying it should be called first, which disambiguates it from 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?

It explicitly instructs when to call the tool ('Call this before query_records') and why, to discover valid filter values 'instead of guessing.' This is a clear, actionable usage directive that names the correlated sibling and the problem avoids.

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