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

Annotations already establish read-only, idempotent, open-world behavior, but the description adds valuable behavioral context beyond those hints: it explains that CMS suppression affects denial/appeal counts, that the data only covers HealthCare.gov states, and that the tool distinguishes missing data from zero denials. This is exactly the kind of interpretive context an agent needs before relying on the data.

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 front-loaded with the key purpose ('What this data covers before you rely on it'), then lists concrete output dimensions. It is slightly dense and uses semicolons and dashes, but every clause serves the user's decision-making and no filler is present.

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 no-parameter tool with no output schema, the description is unusually complete: it names the output contents (plan year, issuer/state counts, included states, suppressed counts), explains the scope limitation (HealthCare.gov states only), and gives the practical interpretation for distinguishing missing data from zero denials. An agent has enough context to call and interpret this tool correctly.

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 tool has zero parameters and schema coverage is 100%, so the baseline is high. The description does not need to explain parameter syntax, but it does explain what the data covers and what the output conceptually represents, which gives meaning to the tool despite the empty input 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 states a clear, specific purpose: this tool reports coverage metadata about insurer denial data—plan year, issuer/state counts, included states, and suppressed-figure counts. It explicitly frames itself as a scoping tool ('Use to check whether a state or insurer is in scope'), which distinguishes it from sibling tools like insurer_denial_rates and insurer_denial_profile.

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 explicit when-to-use guidance: 'Use to check whether a state or insurer is in scope at all, and to tell "we hold no data for this state" apart from "this state's insurers deny nothing"'. It clearly conveys the intended decision-making context, though it does not explicitly name alternative tools or state when not to use it.

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