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

With no annotations, the description carries the transparency burden. It includes a behavioral caveat: 'Note: not adopting the bulletin does NOT mean a state is unregulated — check other_activity in the response.' This clarifies interpretation of results. However, it does not explicitly state whether the operation is read-only or has side effects, though a lookup implies safety.

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 concise, using two sentences plus a brief note. It front-loads the purpose, lists specific data points returned, and adds a crucial caveat in a clear separate sentence. No redundant or vague wording.

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 (single parameter, no output schema), the description fully covers what the tool does, what it returns (citations, dates, status), and includes an important interpretive note. It is complete for a lookup tool without needing an output schema.

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 schema already fully describes the parameter (two-letter code or full name, e.g., 'CO' or 'Colorado'), so coverage is 100%. The description adds only that it is a 'US jurisdiction,' which is implicit from the schema's state code example. It does not provide additional semantic details beyond the schema, so baseline 3 is appropriate.

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 function: 'Look up one US jurisdiction's insurance-AI regulatory posture' and enumerates specific aspects (adopted NAIC model bulletin, own framework, other legislation). It distinguishes from sibling tools like list_jurisdictions (which likely enumerates) and dataset_provenance (which likely provides source metadata).

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 explicitly specifies when to use the tool: 'Call this when asked what a specific state requires, whether a state adopted the NAIC AI model bulletin, or what AI insurance legislation a state has.' This provides clear, actionable conditions and implies it is not for listing all jurisdictions.

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