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Glama

US Real Estate Data

get_home_prices

Get US home price data. Returns national median home price (FRED MSPUS) or metro-level Zillow ZHVI data. Includes current value, YoY change, and 12-month history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoData type: national or metro (default: national)national
limitNoNumber of historical data points (default 12)
metroNoMetro area name (e.g. "New York, NY", "Los Angeles, CA"). If omitted, returns national data.
stateNo2-letter state code to get all metros in a state (e.g. TX, CA).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description provides some behavioral context (what data is returned) but lacks details on authorization, rate limits, error behavior, or side effects. It is adequate but incomplete.

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?

The description is a single comprehensive sentence that efficiently conveys the tool's main features and output. It is concise without being overly terse.

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

Completeness2/5

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

The description lacks details on parameter interactions (e.g., how metro and state affect results) and does not explain the structure of the output. Given the tool's moderate complexity and lack of output schema, the description is insufficient for complete understanding.

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 the schema already documents all parameters. The description adds no additional meaning beyond the schema's brief descriptions, so baseline score of 3 is appropriate.

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?

The description clearly states that the tool retrieves US home price data from specific sources (FRED MSPUS for national, Zillow ZHVI for metro) and returns current value, YoY change, and 12-month history. However, it does not differentiate from sibling tools like get_market_indicators or get_rental_data.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention any conditions or prerequisites for use, nor does it exclude scenarios where other tools would be more appropriate.

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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TDQS

A3.7/5.0
Disambiguation5/5

Each tool targets a distinct aspect: home prices, market indicators, and rental data. No overlap in purpose.

Naming Consistency5/5

All tools use consistent 'get_' prefix with snake_case naming, following a clear verb_noun pattern.

Tool Count4/5

Three tools is a compact but reasonable set for a focused real estate data server, covering key areas without being too thin.

Completeness4/5

Covers home prices, rental data, and major market indicators. Minor gaps like sales volume or inventory do not significantly hinder common use cases.

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