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lzinga

US Government Open Data MCP

fbi_crime_summarized

Read-only

Retrieve summarized FBI UCR crime data at national, state, or agency level, covering 10 offense categories with year-by-year counts and rates.

Instructions

Get summarized UCR crime data from the FBI at national, state, or agency level. Covers 10 offense categories: V (violent crime), P (property crime), HOM (homicide), RPE (rape), ROB (robbery), ASS (aggravated assault), BUR (burglary), LAR (larceny/theft), MVT (motor vehicle theft), ARS (arson). Returns year-by-year data with counts and rates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offenseYesUCR offense code: 'V' (Violent Crime), 'P' (Property Crime), 'HOM' (Homicide), 'RPE' (Rape), 'ROB' (Robbery), 'ASS' (Aggravated Assault), 'BUR' (Burglary), 'LAR' (Larceny/Theft), 'MVT' (Motor Vehicle Theft), 'ARS' (Arson)
stateNoTwo-letter state abbreviation for state-level data
oriNoAgency ORI code for agency-level data (e.g., 'WASPD0000')
from_yearNoStart year (default: 5 years ago)
to_yearNoEnd year (default: current year)
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds 'Returns year-by-year data with counts and rates,' which provides behavioral context beyond the readOnlyHint annotation. However, it does not mention rate limits, data freshness, or pagination behavior. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first sentence defines purpose and scope; second sentence lists categories and output format. Every phrase earns its place. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's 5 parameters, high schema coverage, and absence of output schema, the description adequately explains the offense categories, output structure (year-by-year with counts and rates), and levels of aggregation. It could mention default year ranges, but those are in schema. Overall, it is sufficiently complete for an agent to understand the tool's capability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by stating the data includes counts and rates and covers national/state/agency levels, complementing the schema's parameter descriptions. It explains the context of the offense codes (UCR categories) that the schema already lists, but the overall meaning is enhanced.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get summarized UCR crime data from the FBI at national, state, or agency level' and lists the 10 specific offense categories. This is a specific verb+resource description that distinguishes it from sibling tools like fbi_nibrs or fbi_arrest_data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide guidance on when to use this tool versus alternatives, such as fbi_nibrs for detailed incident-level data or fbi_expanded_homicide for deeper homicide details. No when-to-use or when-not-to-use statements are present.

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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