search_fema_disastersSearch FEMA disaster declarations by state, year, or type (DR=Major Disaster, EM=Emergency, FM=Fire Management). Returns declaration number, dates, programs, and designated areas.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Declaration type: DR (Major Disaster), EM (Emergency), FM (Fire Management) | |
| year | No | Fiscal year declared | |
| limit | No | Max results (default 50) | |
| state | Yes | Two-letter state code (e.g., CA) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already confirm read-only and open-world behavior, so the description's job is to add context. It does so by specifying the returned data (declaration number, dates, programs, designated areas). This is helpful beyond annotations, though it omits details like pagination or error handling.
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?
Two sentences with no wasted words: the first explains the search capability and filters, the second lists return fields. Information is front-loaded and efficient.
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 complexity (4 params, no output schema), the description covers the purpose, filters, and return fields. It lacks details on pagination, ordering, or empty results, but the schema's limit parameter and annotations help fill gaps.
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 each parameter. The description restates the enum meanings but adds no new information beyond what's in the schema. Thus, it provides minimal added value per the baseline for high coverage.
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 verb 'Search' and the resource 'FEMA disaster declarations', and specifies the filtering criteria (state, year, type). It also lists the return fields, making the purpose unambiguous and distinct from sibling tools like 'get_district_disaster_history' which focuses on a district rather than state-level declarations.
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 explains what the tool does but does not provide explicit guidance on when to use it versus alternatives, nor does it mention when not to use it. The context is clear for an agent to infer usage, but no exclusions or alternative tool references are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a clear, specific purpose with detailed descriptions that differentiate them. Prefix patterns like get_district_, search_, analyze_, get_, etc., help an agent easily identify the correct tool for a task.
All tool names use a consistent verb_noun or verb_noun_noun pattern with underscores. The naming convention is uniform across the entire set, with no mixing of styles or ambiguous verbs.
With 47 tools, the count is high but justified by the broad scope of civic data analysis. While some agents might find the sheer number overwhelming, the tools are organized into clear categories (district profiles, searches, analyses) that make navigation feasible.
The toolset covers an impressively wide range of domains: legislation, representatives, districts, voting, committees, campaign finance, lobbying, federal spending, regulations, environment, energy, healthcare, housing, disaster, banking, consumer complaints, crime, vehicles, and more. There are no obvious missing operations for a civic data platform.