get_district_disaster_historyDisaster history for a congressional district: FEMA declarations, recurring hazard types, total assistance amounts. Cross-references with USASpending disaster relief in the district.
| Name | Required | Description | Default |
|---|---|---|---|
| stateCode | Yes | Two-letter state code (e.g., FL) | |
| yearsBack | No | Years of history (default 10) | |
| districtNumber | Yes | District number (0 for at-large) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. Description adds context about the included data types (FEMA declarations, hazard types, assistance amounts) and external cross-reference (USASpending), though it does not disclose data freshness or limitations.
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, front-loaded with key information, no redundant phrasing. Every sentence adds value.
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 no output schema, description adequately covers return content (FEMA declarations, hazard types, assistance, USASpending cross-reference). Missing details like pagination or date range format, but sufficient for basic understanding.
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 100%, so baseline is 3. Description does not add extra meaning beyond the schema's parameter descriptions; it focuses on describing the output instead.
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?
Description clearly states the tool provides disaster history for a congressional district, listing specific data elements (FEMA declarations, hazard types, assistance amounts) and cross-references with USASpending. It distinguishes from siblings like search_fema_disasters by focusing on district-level aggregation.
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?
Description implies usage when district-level disaster history is needed but does not explicitly state when to use this tool over alternatives (e.g., search_fema_disasters) or 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.
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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.