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

The description adds behavioral context regarding the nature of the data, explaining that subjects are 'raw' and 'the fine-grained categories the legislatures themselves assign to bills,' which is valuable context beyond the standard API call structure. Annotations already cover safety (readOnlyHint: true), meaning the description is providing high-quality utility context.

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 definition is highly efficient; every clause is necessary, and the logical flow—defining the resource, explaining its context relative to siblings, and then giving usage tips—is optimally structured.

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 complete schema coverage and the safety annotations provided, the description is maximally complete. It leaves no ambiguity about the purpose, limitations, or proper integration of the returned subject IDs.

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?

Since schema description coverage is 100%, the description does not need to elaborate on the parameters. It adds mild semantic value by contextualizing 'state' (jurisdiction) and 'q' (narrow search) but follows the baseline score for fully documented parameters.

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 explicitly states it provides the 'raw subject taxonomy with per-subject bill counts.' It clearly identifies subjects as fine-grained legislative categories, and effectively distinguishes this tool from 'list_topics' which uses 'curated topic slugs'.

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 gives explicit guidance on both when and how to use the tool, advising agents to use it to 'find a subject_id to filter search_bills by' and to 'gauge what a jurisdiction is legislating on.' It also instructs on parameter use: scope with 'state' and narrow with 'q'.

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