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RentReboot Apartment Search

by io.github.imiraoui
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Get neighborhood asking-rent percentiles

get_rent_data
Read-onlyIdempotent

Retrieve aggregate asking-rent percentiles and sample sizes for a published NYC neighborhood. Values are not individual listings or personal data.

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": {
      "area": "<area>"
    },
    "name": "get_rent_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
areaYesNeighborhood name or URL slug, for example Chelsea or upper-west-side
bedroomsNo0 means studio; omit to return all bedroom counts
categoryNoall

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds genuine context by stating the values are aggregated and not personal data, but it says nothing about result shape (which percentiles), rate limits, or failure behavior when the neighborhood is unpublished.

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?

Two tight sentences, front-loaded with the verb and resource; the second sentence is not filler but a meaningful scope/privacy clarification. Nothing is redundant with the title or schema.

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?

For a simple, annotation-covered read tool with no output schema, the description adequately covers what is returned and the aggregation/privacy guarantee. Remaining gaps (category semantics, how to obtain valid neighborhood identifiers) are modest given the schema provides area format examples.

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 only 67%, and the one undocumented parameter, 'category' (enum all/likely/verified), is semantically opaque without explanation. The description mentions no parameters at all, so it does not compensate for the coverage gap or clarify what 'likely' versus 'verified' rents mean.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Retrieve') and a precise resource ('aggregate asking-rent percentiles and sample sizes') scoped to a published NYC neighborhood. It implicitly separates itself from listing-oriented siblings via 'not individual listings or personal data', but it never names an alternative tool, so differentiation is inferential rather than explicit.

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?

The 'published NYC neighborhood' phrasing implies a precondition (the neighborhood must exist/published) and the scope note tells the agent this is a statistics lookup rather than a listing search. However, there is no explicit when-to-use/when-not or pointer to a sibling for resolving neighborhood names, so the agent must infer routing.

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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