search_vehicle_recallsSearch NHTSA vehicle recalls by make, model, and/or year. Returns campaign number, affected component, safety summary, consequence, remedy, and whether to park the vehicle immediately.
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | Vehicle make (e.g., Ford, Toyota) | |
| year | No | Model year | |
| model | No | Vehicle model (e.g., F-150, Camry) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. Description adds return field details but no extra behavioral context (rate limits, auth). Adequate but minimal added value.
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?
Single, well-front-loaded sentence. No wasted words, every element earns its place.
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?
No output schema, but description covers all return fields (campaign number, component, safety summary, etc.). Simple tool with clear scope and optional parameters.
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% (all parameters described). Description provides examples (e.g., Ford, Toyota) but does not add meaningfully beyond 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?
Description clearly states 'Search NHTSA vehicle recalls' with specific verb and resource. It lists return fields and distinguishes from sibling 'search_vehicle_complaints'.
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?
No guidance on when to use this tool vs alternatives like search_vehicle_complaints. Lacks when-not or prerequisite conditions.
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 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.