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

Annotations already provide readOnlyHint and openWorldHint, but the description adds valuable behavioral disclosures: the exact error returned for unknown dataset names, the timeout risk with NFIP queries, and the need for case-sensitive entity names. This goes beyond what annotations convey, giving agents critical operational knowledge.

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 moderately long but every sentence carries actionable information: purpose, examples, specific tips, and error behavior. It is front-loaded with the primary purpose and then provides necessary caveats. Slightly longer than ideal, but no fluff.

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 generic nature, the description covers all critical operational aspects: what it does, when to use it, dataset-specific gotchas, error behavior, and performance warnings. The output schema exists, so return values are documented. Nothing essential is missing for a correct call.

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 schema already documents all six parameters with clear descriptions and examples. The description adds minor context like the propertyState nuance for filters, but this is more about data model quirks than the parameters themselves. No significant new meaning is added beyond the 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 specific verb (query), resource (any OpenFEMA v2 dataset), and clearly positions it as the 'escape hatch' for datasets not covered by convenience tools. It lists concrete dataset examples and distinguishes it from the specialized siblings by framing it as the generic fallback.

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?

Explicitly tells when to use this tool ('datasets the convenience tools do not cover') and provides dataset-specific guidance, such as using propertyState for NFIP Policies and always including a county or ZIP filter to avoid timeouts. It also warns about case-sensitive dataset names and the unknown_dataset error.

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