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nyc-property-intel

nyc-property-intel

by ccedacero
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_rent_stabilization

Retrieve rent stabilization history for a property, showing stabilized unit counts from 2007-2017 and DHCR confirmation status. Use to verify if a building is rent-stabilized.

Instructions

Get rent stabilization history for a property.

Shows stabilized unit counts from 2007-2017 and whether counts are estimated or confirmed by DHCR. Use this to check if a building is rent-stabilized and track unit count changes over time.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "bbl": "<bbl>"
    },
    "name": "get_rent_stabilization"
  }
}

Input Schema

NameRequiredDescriptionDefault
bblYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description provides some behavioral context: it shows historical counts from 2007-2017 and indicates counts can be estimated or confirmed. However, it does not disclose authentication needs, rate limits, error handling, or what happens with invalid BBLs.

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?

Three concise sentences front-load the purpose and key details. Every sentence provides useful information with no redundancy or filler.

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?

For a simple tool with one parameter and no output schema, the description partially explains the output (counts and confirmation status) but lacks details on output structure, error states, or data coverage assumptions. It is moderately complete but could be improved.

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 coverage is 0%, meaning the description does not explain the 'bbl' parameter (e.g., what it stands for, required format, or how to obtain it). The description adds no semantic value beyond the schema's type and required flag.

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 rent stabilization history, specifies the data range (2007-2017) and type (counts, estimated/confirmed). It distinguishes itself from sibling tools like tax info or complaints by focusing on rent stabilization specifically.

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 includes explicit usage guidance: 'Use this to check if a building is rent-stabilized and track unit count changes over time.' It does not mention when not to use it or alternatives, but the context of siblings implies distinct purposes.

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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