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Woodpecker: NYC HPD registration lookup

by help.woodpecker
OverviewDiscussionsAdmin

Open HPD filing violations by borough

open_hpd_filing_violations_by_borough
Read-onlyIdempotent

How many NYC buildings currently have an open HPD violation for a missing property registration or a missing annual bed bug report, by borough, counted from the city's open data.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "open_hpd_filing_violations_by_borough"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, and closed-world characteristics, so the bar is lower. The description still adds real behavioral value by specifying the counting unit (buildings, not violations), the two violation types included, the currency qualifier 'currently', and the data source. It omits refresh cadence and result shape, which keeps it from a 5.

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?

One sentence, front-loaded with the question it answers, with no filler. Every clause (location, condition, violation types, grouping, source) carries information.

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

Completeness4/5

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

Given a no-parameter aggregation tool with rich annotations, the definition is nearly self-sufficient: an agent knows what is counted, how it is grouped, and that it is a current snapshot. The only minor gap is that no output schema exists and the return shape (borough-to-count mapping) is left implicit.

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?

The tool takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate. The description correctly implies the single grouping dimension (borough) rather than implying filters that don't exist.

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?

States a precise verb and resource: counts NYC buildings with an open HPD violation for missing property registration or missing annual bed bug report, grouped by borough, sourced from open data. This is clearly distinguishable from siblings like explain_hpd_filing_requirements (explanation) or get_nyc_building (single-building lookup).

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

Usage Guidelines3/5

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

Usage is implied by the framing 'how many ... by borough' and the qualifier 'currently', which signals a snapshot aggregate query. However, it never explicitly says when to prefer this over explain_hpd_filing_requirements or per-building lookup tools, and offers no exclusions or prerequisites.

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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