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ccedacero

nyc-property-intel

by ccedacero

get_dob_complaints

Retrieve Department of Buildings complaints filed against a property. These public records signal potential construction, safety, or code violations before formal inspections occur.

Instructions

Get DOB complaints filed against a property with the Dept of Buildings.

Queries the DOB Complaints Received dataset (NYC Open Data `eabe-havv`).
Complaints are filed *before* formal violations are issued — they trigger
DOB inspections and are the earliest public signal of construction, safety,
or code issues at a building.

Key insight: compare this with `get_property_issues` violations. If a
property has many complaints but few violations, DOB may not be inspecting.
If complaints are recent and unresolved, it flags active safety concerns.

Common complaint categories: illegal construction (01), elevator (02),
plumbing (03), illegal conversion (04), boiler (05), structural (06),
facade (07), fire egress (09), work without permit (10), electrical (11).

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

Args:
    address: Street address, e.g. "350 5th Ave, Manhattan".
    bbl: 10-digit NYC BBL. Resolved via BIN lookup for accurate matching.
    category: Filter by complaint category code, e.g. "01" for
              construction without permit, "04" for illegal conversion.
    status: Filter by status keyword, e.g. "OPEN", "CLOSED",
            "REFERRED TO DA".
    since_year: Return only complaints from this year onward.
    limit: Max complaints to return (1–100, default 25).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressNo
bblNo
categoryNo
statusNo
since_yearNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. It mentions the data source and that complaints precede violations, but does not disclose any side effects, rate limits, or authentication needs. However, as a read-only query, the lack of behavioral notes is acceptable but not exemplary.

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?

Well-structured with clear sections, parameter list, and contextual insights. Every sentence adds value without redundancy. Appropriate length given tool complexity.

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?

Covers all necessary aspects: dataset origin, usage context, parameter details, interpretation guidance, and connection to sibling tools. With an output schema present, return values are implicitly covered.

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 coverage is 0%, but description adds comprehensive meaning: explains address/BBL relationship, lists common category codes with examples, specifies status keywords, bounds for limit, and since_year semantics. This far exceeds what the raw 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?

Clearly states 'Get DOB complaints filed against a property with the Dept of Buildings', specifies the dataset (NYC Open Data eabe-havv), and contrasts with sibling `get_property_issues`, distinguishing when to use each.

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?

Explicitly advises when to use this tool vs `get_property_issues`, provides interpretation guidance (complaints vs violations), and specifies to provide either address or BBL, with no duplication.

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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