US Real Estate Data

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

With no annotations provided, the description must convey the behavioral profile. It discloses core outputs and data sources, but does not address potential limitations, data update frequency, or how parameters like 'state' modify the result beyond the schema. It also implies a fixed 12-month history, while the 'limit' parameter allows customization, which could be slightly misleading.

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 deliver the essential purpose and key data points without fluff. The structure front-loads the main function and follows with concise output details.

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 tool has four parameters and no output schema, but the description covers the primary return contents (current value, YoY change, 12-month history). The schema's per-parameter documentation handles the conditional logic, so the description is reasonably complete for an AI agent to make a basic call, though it does not discuss result formatting or edge cases.

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?

The input schema has 100% coverage, so the description is not required to explain parameters; however, it adds meaningful context by naming the exact data sources behind the 'type' parameter (FRED MSPUS vs. Zillow ZHVI). It does not elaborate on 'limit' or 'state' beyond what the schema already provides, so it meets the baseline without significantly exceeding it.

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 a specific verb ('Get') with a resource ('US home price data'), identifies data sources (FRED MSPUS, Zillow ZHVI), and distinguishes itself from siblings like rental data or market indicators. It also outlines key output features (current value, YoY change, history), making the purpose unambiguous.

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 the tool is for home price queries and differentiates national vs metro levels, but it does not explicitly state when to choose this tool over get_market_indicators or get_rental_data, nor does it mention exclusions. The context is clear for a home-price-focused query, but there are no explicit alternatives or when-not-to-use cues.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries full burden. The verb 'Get' suggests a read-only operation, and the description lists the data source (FRED) and indicators, but it does not disclose return format, historical depth, or behavior for invalid series. Basic but adequate for a simple read tool.

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, front-loaded sentence that efficiently communicates the tool's purpose and data source. No wasted words, directly actionable.

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?

With no output schema, the description could explain what the response contains (e.g., time series, latest values). It lists the indicators but omits response structure. For a simple one-param tool, it is adequate but leaves room for ambiguity.

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 a well-described 'series' parameter listing valid FRED IDs. The description adds human-readable names (housing starts, building permits, etc.) that map to these IDs, providing meaning beyond the raw schema.

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 key US housing market indicators from FRED, listing specific indicators. This distinguishes it from siblings like get_home_prices and get_rental_data by covering a broader set of market indicators.

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 fetching housing market indicators but does not explicitly compare with sibling tools or state when to choose this over get_home_prices or get_rental_data. No exclusions or alternative guidance are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It mentions the data source and return values, which is helpful, but does not disclose potential side effects, rate limits, or the fact that metro is ignored (national data always returned). It does not contradict anything, but there is room for more transparency about limitations.

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 two sentences, front-loaded with the key verb and resource, and every phrase earns its place. It is concise and easy to parse, with no redundancy or filler.

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?

With no output schema and only a simple input schema, the description needs to convey what the tool returns. It lists the key outputs, but it does not describe the output structure or explicitly note that metro is disregarded. Given the lack of annotations and output schema, the description is not fully complete for an agent to anticipate all behaviors.

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?

The schema covers 100% of parameter descriptions, including 'limit' and 'metro' with their defaults and a hint that metro is optional. The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 applies.

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 action ('Get'), the resource ('US rental market data'), the source ('FRED BLS CPI series'), and the expected outputs ('national rent index, YoY change, and monthly history'). This differentiates it from sibling tools like get_home_prices and get_market_indicators by focusing specifically 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 provides clear context for when to use the tool (when US rental market data from FRED BLS CPI series is needed). However, it does not explicitly mention alternatives or exclusions, such as directing users to get_home_prices for home prices, nor does it note that metro parameters are ignored. It's useful but lacks explicit guidance beyond the core use case.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.