Per-county FEMA disaster-declaration detail for one hurricane event (declaration type/date, incident window, IA/PA/IH/HM program flags). Filter by state. $0.10 per query. Mirrors GET /v1/cat/counties. Free discovery: /v1/cat/events, /v1/cat/sample.
| Name | Required | Description | Default |
|---|---|---|---|
| event | Yes | ||
| state | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It notes the tool mirrors a GET endpoint and has a cost, but does not mention authentication requirements, rate limits, or whether it is read-only (though 'mirrors GET' implies idempotence). Lacking these details, the description provides moderate transparency.
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 three sentences, each serving a distinct purpose: core functionality, parameters, and cost/discovery. No fluff, and the most critical information is front-loaded.
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 has only two parameters and no complex output schema, the description covers the main return fields and cost. It lacks error handling or pagination notes, but the addition of free discovery endpoints partially compensates for completeness.
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 coverage is 0%, so the description must compensate. It explains the 'state' parameter (filter by two-letter code) and mentions 'event' implicitly. However, it does not define what constitutes an 'event' (e.g., a hurricane name or ID) or its required format, leaving ambiguity.
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 provides per-county FEMA disaster-declaration details for one hurricane event, listing specific fields (declaration type/date, incident window, IA/PA/IH/HM program flags). This distinguishes it from sibling tools which cover broader categories like exposure, impact, or carrier financials.
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 mentions filtering by 'state' and cost ($0.10 per query), and suggests free discovery endpoints for exploration. However, it does not explicitly compare this tool to alternatives like get_cat_exposure or get_cat_impact, so an agent lacks clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose, covering different data types (carriers, financials, catastrophes, hazard, etc.) and actions. Even closely related tools like compare_states and compare_carrier_states differ in scope (state comparison vs. carrier footprint), so no ambiguity exists.
The vast majority follow a verb_noun pattern with 'get_' for retrieval and 'list_' for enumeration. A few tools use 'compare_' and 'check_', which are semantically appropriate but introduce slight inconsistency. Overall, the pattern is predictable and readable.
With 40 tools, the count is well above the typical 3-15 range, making it feel heavy. While the domain justifies many endpoints, the presence of over 10 dedicated sample tools adds unnecessary bloat. Consolidation of sample tools could improve scoping.
The server covers a wide range of insurance data domains: eligibility, carriers, financials, catastrophe, hazard, litigation, premium, rates, syndicates, and enforcement. It includes discovery tools and sample data. As a read-only API, it is comprehensive, though a tool for policy-level detail might be a minor gap.
