AgentProperty — Real estate & housing intelligence
Server Details
U.S. real estate intelligence — Zillow home price indices by metro, FRED housing market indicators (mortgage rates, starts, permits, Case-Shiller), and rental CPI data.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.7/5 across 3 of 3 tools scored.
Each tool targets a distinct aspect of the housing market: home prices, general market indicators, and rental data. No overlap in purpose.
All tools follow a consistent verb_noun pattern (get_home_prices, get_market_indicators, get_rental_data), making the set predictable.
Three tools is a small but appropriate set for a focused data API covering key housing metrics. Could be expanded with additional indicators but is reasonable.
The tools cover core housing data but omit common indicators like inventory, days on market, or granular geography. Major operations like listing or search are absent, which aligns with the stated purpose of intelligence rather than listings.
Available Tools
3 toolsget_home_pricesAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Data type: national or metro (default: national) | national |
| limit | No | Number of historical data points (default 12) | |
| metro | No | Metro area name (e.g. "New York, NY", "Los Angeles, CA"). If omitted, returns national data. | |
| state | No | 2-letter state code to get all metros in a state (e.g. TX, CA). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description partially covers behavior by stating return fields (current value, YoY change, 12-month history). However, it omits details like data update frequency, error handling, or behavior when conflicting parameters are provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler, front-loaded with purpose. Every word contributes necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main function but lacks detail on parameter interactions (e.g., state vs metro) and does not reference sibling tools. Given no output schema, the return description is helpful but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the description still adds value by linking the 'type' parameter to the data source (national vs Zillow). It also clarifies the output structure (current value, YoY change, history) beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool gets US home price data, specifying two data sources (FRED MSPUS for national, Zillow ZHVI for metro). This clearly distinguishes it from the sibling tools get_market_indicators and 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for national vs metro data but provides no explicit guidance on when to use this tool over siblings. No mention of when not to use or alternatives, though the specificity of home prices offers implicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_market_indicatorsBInspect
Get key US housing market indicators from FRED: housing starts, building permits, median home price, 30-year mortgage rate, and Case-Shiller index.
| Name | Required | Description | Default |
|---|---|---|---|
| series | No | Comma-separated FRED series IDs to filter (e.g. "HOUST,MORTGAGE30US"). Valid: HOUST, PERMIT, MSPUS, MORTGAGE30US, CSUSHPINSA. Default: all. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description only states 'get' with no disclosure of rate limits, data freshness, auth requirements, or side effects, placing full burden on description which fails to provide behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with purpose and a concise list of indicators. No wasted words, though slightly more structure could improve scannability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, yet the description does not explain the return format (e.g., time series, latest values). For a simple list tool it is adequate but misses context about data structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter description including valid values and default. The tool description adds no additional meaning beyond the schema, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets key US housing market indicators from FRED and lists five specific indicators (housing starts, building permits, etc.), differentiating it from sibling tools like get_home_prices and 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus siblings, but the listed indicators imply its scope. Lack of when-not-to-use or alternative suggestions lowers the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_rental_dataAInspect
Get US rental market data from FRED BLS CPI series. Returns national rent index, YoY change, and monthly history.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of historical data points (default 12) | |
| metro | No | Metro area (optional; national data returned regardless) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the source and output content, but lacks details on data frequency, potential delays, error handling, or behavior when metro is invalid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence of 17 words. It front-loads the primary action and lists outputs concisely with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with two optional parameters and no output schema, the description gives a solid overview of what is returned. However, it omits details like units, interpretation, and data freshness that would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already defines both parameters. The description does not add meaningful new information about parameters; it only repeats 'metro is optional' implicitly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves US rental market data from a specific source (FRED BLS CPI series) and specifies three key outputs: national rent index, YoY change, and monthly history. It distinguishes from sibling tools like get_home_prices by focusing on rental data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for rental data but does not explicitly state when to use this tool versus siblings. No guidance on when not to use it or which circumstances are ideal.
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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