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dathere

FBI Crime Data MCP Server

by dathere

get_summarized_crime_data

Retrieve summarized SRS crime data—offense rates, actuals, clearances, and population coverage—by offense, geography, and date range. Query yearly or monthly at national, state, or agency level.

Instructions

Get summarized (SRS) crime data including offense rates, actuals, clearances, and population coverage.

Args: offense: SRS offense code (e.g., "V" for Violent Crime, "P" for Property Crime, "HOM", "RPE", "ROB", "ASS", "BUR", "LAR", "MVT", "ARS"). Use get_reference_data for full list. level: Geographic level — "national", "state", or "agency" from_date: Start date in mm-yyyy format (e.g., "01-2020") to_date: End date in mm-yyyy format (e.g., "12-2022") state: Two-letter state abbreviation (required when level is "state") ori: Agency ORI code (required when level is "agency") aggregate: Aggregation level — "yearly" (default, sums monthly into yearly) or "monthly" (monthly granularity). Years with fewer than 12 months of data are flagged in a top-level "_partial_years" key (rates are unweighted averages of the available months).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
oriNo
levelYes
stateNo
offenseYes
to_dateYes
aggregateNoyearly
from_dateYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does so well: it discloses the default aggregation mode, that partial years are flagged under '_partial_years', and that rates are unweighted averages of available months. It stops short of describing pagination, rate limits, or return structure, but the behavioral detail is notably above baseline.

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

Conciseness4/5

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

The summary line is front-loaded and each Args entry earns its place with concrete examples. It is somewhat long, but the added length is informational rather than redundant, so the size is justified.

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?

Because an output schema exists, return values need not be explained, and the description covers all inputs, defaults, and conditional requirements. It could add a brief note on data vintage or caching behavior relative to manage_cache, but for the tool's complexity it is nearly complete.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate and it does: every one of the 7 parameters is documented with format examples ('mm-yyyy'), enumerated offense codes, valid level values, and cross-parameter dependencies between level and state/ori. This is exactly what a low-coverage schema needs.

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

Purpose4/5

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

The description states a specific verb and resource ('Get summarized (SRS) crime data') and enumerates the data domains returned (offense rates, actuals, clearances, population coverage). It partially differentiates from siblings by naming SRS data and pointing to get_reference_data, though it doesn't explicitly contrast with get_arrest_data or get_nibrs_data.

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

Usage Guidelines4/5

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

It gives concrete conditional guidance: 'state' is required when level is 'state', and 'ori' is required when level is 'agency', plus a pointer to get_reference_data for the offense list. No explicit when-not-to-use guidance or sibling comparison is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.