Get the typical home value for a metro or state (Zillow Home Value Index): the latest value plus 1-year and 5-year-ago values and percent change. Pass a region name or id.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 establish this as read-only, idempotent, and non-destructive, so the description only needs to add value beyond that. It does so by disclosing the exact output dimensions: latest value, 1-year and 5-year-ago values, and percent change. This gives useful behavioral expectations without contradicting the annotations.
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 states the purpose, the metric, the returned data points, and the required input without any filler. Every phrase contributes directly to the agent's understanding.
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 simple one-parameter, read-only lookup with no output schema, the description fully covers what the agent needs: the geographic scope, the indexed metric, the time-ago comparisons, and the input format. The schema and annotations handle the remaining details, leaving no critical gap for invoking this tool correctly.
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 fully documents the single `region` parameter with examples and formats, including metro/state names or Zillow region IDs. The description's 'Pass a region name or id' simply restates the schema, adding no new semantic information, so the baseline score 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 uses a specific verb ('Get') and resource ('typical home value for a metro or state'), and identifies the underlying metric as the Zillow Home Value Index. It also enumerates the delivered data points, which clearly distinguishes it from rent-focused and property-specific siblings like realestate_rents and property_value_history.
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 appropriate use case through 'metro or state' and 'typical home value', suggesting aggregate geographic data rather than property-level or rental data. However, it does not explicitly name alternatives or state when to prefer realestate_trend, realestate_rents, or property_value_history, leaving some selection inference to the agent.
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.