search_crime_statisticsFBI UCR crime statistics by state and offense type. Returns actual counts, rates per 100,000, clearances, and national comparison. Offense types: violent-crime, property-crime, HOM, RPE, ROB, ASS, BUR, LAR, MVT, ARS.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Year (default: most recent available) | |
| state | Yes | Two-letter state code (e.g., CA) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context by detailing return fields (counts, rates, clearances, national comparison) and listing offense types, which helps the agent understand what the tool provides without contradicting annotations.
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 concise with two sentences. The first sentence clearly states the purpose and return fields; the second lists offense types. It is front-loaded and efficient, though the status of offense type as a parameter could be clarified.
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 simplicity (2 parameters, no output schema), the description explains returns and lists offense types, but fails to clarify how to query a specific offense type or that all offense types are returned by default. This gap reduces 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 100% for state and year, so baseline is 3. However, the description mentions filtering by 'offense type' and lists offense codes, but the schema does not include an offense type parameter. This introduces ambiguity about how to specify offense type, reducing clarity.
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 searches FBI UCR crime statistics by state and offense type, and lists return fields (counts, rates, clearances, national comparison). It uniquely identifies the tool's function among siblings, as no other tool addresses crime statistics.
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 provides clear context for when to use the tool (to get crime statistics by state and offense type) but does not explicitly state when not to use it or mention alternatives. Since there are no sibling crime tools, this omission is acceptable.
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.