Get the monthly time series of home values (ZHVI) or rents (ZORI) for a metro or state, to chart or analyze the trend.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | No | 'home_value' (ZHVI, default) or 'rent' (ZORI). | |
| months | No | How many recent months to return (default 24, max 360). | |
| region | Yes | Metro or state name (e.g. 'Austin, TX', 'Houston', 'Texas') or a Zillow region id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context by specifying that the result is a monthly time series and that it can represent either home values or rents, but it does not disclose output structure, regional data availability, or other 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 a single, front-loaded sentence that efficiently conveys the action, data granularity, supported metric types, geographic scope, and intended use. Every phrase earns its place 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 read-only tool with three well-documented parameters and strong annotations, the description covers the core invocation context well. The main gap is the lack of detail about the returned time-series structure, but 'monthly time series' is sufficient for many charting and analysis use cases, and no output schema exists to fill that gap.
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 region, metric, and months are already fully documented in the input schema. The description mostly restates the metric and region concepts without adding examples, defaults, or extra semantic detail, which matches the baseline for complete schema coverage.
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 operation ('Get the monthly time series'), the resources (home values/ZHVI and rents/ZORI), and the geographic scope (metro or state). It does not explicitly differentiate itself from sibling tools like realestate_home_values or realestate_rents, but the focus on 'trend' and monthly series makes the purpose clear.
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 phrase 'to chart or analyze the trend' implies when this tool is appropriate, but the description does not explicitly say when to use it instead of related tools such as realestate_home_values or realestate_rents. There are no exclusions or alternative-tool pointers, so usage guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.