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ccedacero

nyc-property-intel

by ccedacero

get_311_complaints

Retrieve 311 service request complaints filed at or near a NYC property address to assess neighborhood quality and building distress, covering noise, rodents, heat, and 200+ complaint types.

Instructions

Get 311 service request complaints filed at or near a property address.

Queries the local 311 database (NYC Open Data). Covers noise, rodents,
illegal dumping, graffiti, heat/hot water, illegal parking, street
conditions, and ~200 other complaint types.

311 data is a leading-indicator for neighborhood quality and building
distress — complaints are filed *before* violations are issued. High
complaint volume at an address is a red flag for active tenant issues.

Provide either `address` OR `bbl` (not both).

Args:
    address: Street address, e.g. "37-06 80th Street, Queens".
    bbl: 10-digit NYC BBL. Resolved to street address via PAD table.
    complaint_type: Filter by complaint type keyword, e.g. "NOISE",
                    "RODENT", "HEAT", "ILLEGAL PARKING". Case-insensitive.
    since_year: Return only complaints from this year onward (2010–present).
    status: Filter by status: "Open" or "Closed".
    limit: Max complaints to return (1–100, default 30).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressNo
bblNo
complaint_typeNo
since_yearNo
statusNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It explains that complaints are filed before violations, high volume is a red flag, and includes default limit and case-insensitive filtering. It does not mention pagination or rate limits but provides sufficient behavioral context.

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?

The description is well-structured with a clear header, explanatory paragraph, and a bullet-like parameter list. It is concise with no redundant information, every sentence adds value.

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?

Despite having an output schema (not shown), the description covers the tool's purpose, parameters, and the nature of 311 data. For a tool with 6 parameters, it provides sufficient context for correct usage without relying on the output schema.

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. It thoroughly explains all six parameters: address, bbl, complaint_type, since_year, status, and limit, including constraints and examples. This adds significant value beyond the schema.

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 it retrieves 311 service request complaints at or near a property address, specifies the data source (NYC Open Data), and lists complaint categories. It distinguishes from siblings like get_hpd_complaints or get_dob_complaints by focusing on 311 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?

Provides guidance on using either address or bbl, includes examples of complaint types and parameters, and explains the significance of 311 data as a leading indicator. However, it lacks explicit alternatives or when-not-to-use scenarios compared to sibling tools.

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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