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ccedacero

nyc-property-intel

by ccedacero

search_neighborhood_stats

Aggregate neighborhood statistics on property stock, sales market, violation patterns, and rent stabilization for comparing areas and identifying investment opportunities.

Instructions

Get aggregate neighborhood statistics for market research and area analysis.

Combines property stock data, sales market activity, violation patterns,
and rent stabilization counts at the zip code or neighborhood level.
Use this to compare areas, identify investment hotspots, or understand
a neighborhood's character before drilling into individual properties.

At least one of zip_code or neighborhood is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zip_codeNo
neighborhoodNo
building_classNo
monthsNo
include_quarterly_trendsNo
include_violationsNo
include_rent_stabilizationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries full burden. It states that the tool combines multiple data sources into aggregate statistics, but does not disclose behavioral details like rate limits, response structure, or that it returns only aggregates (no individual records). The existence of an output schema mitigates some gaps.

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 relatively concise (two sentences plus a usage note) and front-loads the main purpose. It could benefit from a more structured format (e.g., bullet points) but avoids unnecessary detail.

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

Completeness3/5

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

Given the tool has 7 parameters and an output schema, the description adequately covers the purpose and main inputs but omits details about boolean flags and months parameter. It completes the context for the tool's role as an aggregate querier, but not fully for parameter usage.

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

Parameters2/5

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

Schema description coverage is 0%, yet the description only explains zip_code and neighborhood. Five other parameters (building_class, months, include_quarterly_trends, include_violations, include_rent_stabilization) are not described, leaving the agent to infer from names alone. This is a significant gap for a tool with 7 parameters.

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 aggregate neighborhood statistics' and lists the combined data types (property stock, sales, violations, rent stabilization). It distinguishes from sibling tools that return specific records (e.g., get_evictions, get_rent_stabilization) by emphasizing aggregation and area analysis.

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?

The description provides explicit use cases: compare areas, identify investment hotspots, understand neighborhood character before drilling into individual properties. It also notes that at least one of zip_code or neighborhood is required. However, it does not explicitly name alternative sibling tools for specific data needs.

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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