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compare_areas

Read-only

Analyze and RANK 2-12 ZIPs or addresses side by side on the signals TerraVue has: home-price appreciation forecast (and how each compares to the national average), school rating, walkability, education level, and water quality. Returns the correct city name per ZIP, so locations are never guessed or mislabeled.

Use for "which areas have the best appreciation / schools / upside." TerraVue has no
listing inventory and cannot discover ZIPs on its own — to scan a metro, pass that
metro's ZIP codes (the ranking then covers exactly what you passed).

Input Schema

NameRequiredDescriptionDefault
bedroomsNoBedroom count, used to select the matching market-rent estimate for the ZIP. Defaults to 2.
addressesYes2 to 12 US addresses or bare 5-digit ZIPs to rank side by side. TerraVue has no listing inventory and cannot discover ZIPs on its own — to scan a metro, pass that metro's ZIP codes explicitly, and the ranking covers exactly what you passed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / addresses / description
      Added value: +"2 to 12 US addresses or bare 5-digit ZIPs to rank side by side. TerraVue has no listing inventory and cannot discover ZIPs on its own — to scan a metro, pass that metro's ZIP codes explicitly, and the ranking covers exactly what you passed."
    • addedInput schema / properties / bedrooms / description
      Added value: +"Bedroom count, used to select the matching market-rent estimate for the ZIP. Defaults to 2."
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds non-obvious behavior beyond that: TerraVue has no listing inventory, cannot discover ZIPs on its own, the ranking covers exactly what was passed, and it returns the correct city name per ZIP. These are real behavioral constraints, not restated annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and signal list, then a second paragraph for usage. Every sentence earns its place, though the no-listing-inventory caveat is somewhat verbose and overlaps with the schema's addresses description.

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?

With no output schema, the description carries the return-value burden and does so by stating it returns ranked signals plus the correct city name per ZIP. It is largely complete for a two-parameter read tool, though it could say more about ranking direction or output shape.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters (addresses, bedrooms) are already documented, including the 2-12 range and the bedroom-to-rent-estimate link. The description restates the range and the ZIP-discovery caveat but adds no new syntax or format meaning, so the baseline 3 applies.

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 specific verb (analyze and RANK) and resource (2-12 ZIPs or addresses side by side) and enumerates the exact signals computed (appreciation forecast, school rating, walkability, education level, water quality). The multi-location scope implicitly but clearly separates it from single-location siblings like analyze_address and affordability.

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?

Gives an explicit trigger ('Use for "which areas have the best appreciation / schools / upside"') and a clear operational constraint (pass a metro's ZIPs explicitly because TerraVue cannot discover ZIPs). It stops short of naming an alternative sibling or stating when not to use it, so it is clear context without exclusions.

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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