Get the typical asking rent for a metro or state (Zillow Observed Rent 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 declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context by naming the data source and exactly which time-series values are returned. It does not describe edge-case behavior for invalid region inputs, but for a read-only lookup this is not a major gap.
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 tight sentences with no filler. The core what-it-does is front-loaded, and the input instruction is the only extra needed. Every word earns its place.
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?
Given one required parameter, a full schema, and safety annotations, the description is complete enough. It explains what data is returned in enough detail to compensate for the missing output schema, and the parameter example list is already in the schema.
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%: the schema already details the region parameter with formats and examples ('Austin, TX', 'Houston', 'Texas', Zillow region id). The description's 'Pass a region name or id' largely repeats the schema, adding no significant new meaning beyond what is already documented.
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?
States a specific verb and resource: 'Get the typical asking rent for a metro or state (Zillow Observed Rent Index)'. It also specifies the exact data returned (latest, 1-year-ago, 5-year-ago values, percent change), which clearly distinguishes it from sibling real-estate tools that cover home values, search, or trends.
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 gives clear context for when to use the tool: whenever a typical asking rent for a metro or state is needed. It does not explicitly name alternatives or exclusions, but the narrow scope ('metro or state', 'Zillow Observed Rent Index') is enough for an agent to select it appropriately among siblings.
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.