DoorProfit Crime & Neighborhood Data

Get Crime Data & Safety Score

get_crime_data

Read-onlyIdempotent

Crime safety grade and incident history for one US address or coordinate pair: a letter score (A+ safest through F), a per-crime-type breakdown against the national average, and the individual incidents recorded within 1000ft over a rolling 90-day window. Use this for "is this address/neighborhood safe?" questions. Do NOT use it for demographics, income, rent or schools (use get_neighborhood_data) or for registered offenders (use search_offenders) — neither is included here. Pass either address or lat+lng, not both. US locations only; requires a DoorProfit API key and consumes 1 call from that key's quota, except for the free test address "500 Main St, Dallas, TX 75202".

Input Schema

TableJSON Schema

Name	Required	Description
`lat`	No	Latitude (use together with lng, as an alternative to address).
`lng`	No	Longitude (use together with lat, as an alternative to address).
`address`	No	Full US street address, e.g. "500 Adams St, Tulsa, OK". Provide either address OR lat+lng.

Output Schema

TableJSON Schema

Name	Required	Description
`success`	Yes
`location`	No	The resolved location for the query.
`incidents`	No	Recent crime incidents near the location.
`test_mode`	No	True when querying the free test address.
`crime_score`	No	Letter safety grade from A+ (safest) to F.
`crime_numeric`	No	0-1 scale where 1 is safest.
`crime_breakdown`	No	Per-crime-type comparison vs the national average (e.g. assault, burglary, theft).
`crime_description`	No	Human-readable explanation of the score.

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safe-read profile is well covered. The description adds meaningful context beyond annotations: quota consumption (1 call per key), a free test address exception, and data scope (rolling 90-day window, 1000ft radius). Doesn't describe pagination/limits on incident lists, but covers key behavioral traits.

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?

Reasonably compact single paragraph covering purpose, exclusions, alternatives, and constraints. Every sentence earns its place, though it is denser than strictly necessary and could be broken into shorter sentences for scanability. It is front-loaded with the core purpose then fills in usage nuance.

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

Completeness5/5

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

An output schema exists so return-value explanation is not the description's job. For a moderate-complexity query tool with rich annotations, the description covers purpose, scope, constraints, quota cost, free test address, exclusions, and alternatives. Fully adequate for an agent to select and correctly invoke this tool.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all three parameters well (lat, lng, address with mutual exclusivity). The description reinforces that address OR lat+lng should be passed, not both, and notes US-only locations. This aligns with the baseline-3 for high schema coverage; the description adds minimal supplementary meaning beyond reinforcing exclusivity.

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?

Purpose is explicit and specific: returns a crime safety grade (A+ through F), per-crime-type breakdown versus national average, and incidents within 1000ft over 90 days. It names the exact resource (crime data & safety score) and clearly distinguishes from siblings by spelling out exclusions.

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

Usage Guidelines5/5

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

States when to use ('is this address/neighborhood safe?' questions) and explicitly names alternatives for excluded topics: get_neighborhood_data for demographics/income/rent/schools and search_offenders for registered offenders. Also warns against passing both address and lat+lng, and specifies US-only and API key/quota requirements.

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

All DoorProfit Crime & Neighborhood Data tools Inspect

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TDQS

A4.4/5.0

Disambiguation4/5

The four tools are largely distinct: crime/safety data, neighborhood demographics, offender search, and API usage. The descriptions explicitly cross-reference which tool to use for which question, reducing confusion. However, get_crime_data and search_offenders both relate to safety in a place, so an agent might briefly hesitate, though the boundary (risk grade vs. individuals) is clearly drawn in both descriptions.

Naming Consistency4/5

Three of four tools follow a consistent get_/search_ + noun pattern with get_crime_data and get_neighborhood_data being very parallel. search_offenders deviates slightly by using search_ instead of get_, and get_api_usage is a different resource type (meta/tooling), but these deviations are reasonable and readable.

Tool Count4/5

Four tools is a reasonable scope for a data-lookup server covering crime, demographics, offenders, and usage. It sits at the low end but each tool covers a distinct, large data domain with query options (location mode, name mode, pagination), so none feels trivial or like filler.

Completeness4/5

The surface covers the three main residential-data question types (crime/safety, demographics, registered offenders) plus API monitoring, which is a coherent set for a neighborhood-intelligence API. Minor gaps exist—e.g., no schools data despite being casually mentioned in get_crime_data's exclusion note, and no bulk/list or batch endpoints—but core workflows are fully covered.

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