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DoorProfit Crime & Neighborhood Data

Get Neighborhood Demographics & Economics

get_neighborhood_data
Read-onlyIdempotent

Profile of the neighborhood (or census block group) containing one US address or coordinate pair: demographics, median household income, home values, rent by bedroom count, education attainment, cost-of-living indices, climate and natural-disaster risk. Use this when the question is about who lives in an area or what it costs to live there. Do NOT use it for crime or safety scores (use get_crime_data) — no crime data is returned here. Pass either address or lat+lng, not both. The neighborhood field is null where no data exists for the location. 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
NameRequiredDescriptionDefault
latNoLatitude (use together with lng, as an alternative to address).
lngNoLongitude (use together with lat, as an alternative to address).
addressNoFull US street address, e.g. "500 Adams St, Tulsa, OK". Provide either address OR lat+lng.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successYes
locationNoThe resolved location for the query.
test_modeNoTrue when querying the free test address.
neighborhoodNoNeighborhood data; null when no data exists for the location.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is established. The description adds valuable behavioral context beyond annotations: quota consumption (1 call), requires an API key, the neighborhood field can be null when no data exists, and a free test address exception. This is strong supplementary transparency.

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 description is information-dense but organized logically: what's returned, when to use, when not to use, input rules, edge case (null neighborhood), and constraints (US only, API key, quota). Front-loaded with the data profile. Slightly long but every sentence carries distinct value with no redundancy.

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?

For a read-only, idempotent, open-world tool with an output schema and 100% schema coverage, the description covers purpose, boundaries, input constraints, edge cases, and operational requirements. There's an output schema present, so return-value documentation is handled structurally. Nothing essential is missing for this moderate-complexity tool.

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% (all 3 parameters documented in schema), so baseline is 3. The description adds the mutually-exclusive constraint between address and lat+lng and specifies address format with an example, going slightly beyond schema. However, it doesn't describe lat/lng ranges or precision requirements beyond what schema provides.

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 the verb+resource (Profile of the neighborhood/census block group) and enumerates the specific data dimensions returned: demographics, income, home values, rent, education, cost-of-living, climate/disaster risk. It also explicitly distinguishes itself from get_crime_data, a sibling tool, by stating what it does NOT return.

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?

Provides explicit when-to-use guidance ('Use this when the question is about who lives in an area or what it costs to live there'), explicit when-not-to-use with the named alternative ('Do NOT use it for crime or safety scores (use get_crime_data)'), and input constraints ('Pass either address or lat+lng, not both', 'US locations only'). This is exemplary usage guidance.

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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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.

Resources