Find real-estate markets (metro areas or states) by name and get each one's latest typical home value (Zillow Home Value Index). Use this to discover the region name/id before calling realestate_home_values, realestate_rents, or realestate_trend.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Optional filter: 'metro' or 'state'. | |
| limit | No | Max rows (default 10, max 50). | |
| query | Yes | Name fragment, e.g. 'Austin', 'Bay Area', 'Texas'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds useful behavior beyond that: the search returns the latest typical home value and yields region name/id for downstream calls. It does not detail pagination or result ordering, but that is minor for a safe read-only search 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 compact and front-loaded: the action and result come first, followed by a one-sentence usage workflow. Every sentence earns its place, and there is no redundant repetition of annotation or schema 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?
For a low-complexity search tool with complete schema documentation and rich safety annotations, this description is nearly sufficient. It explains the discovery workflow and implies the return contains region identifiers and home values. Without an output schema, a little more specificity about the exact returned fields would be helpful, but the agent has enough to invoke and chain 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?
Schema description coverage is 100%, so the baseline is 3. The description echoes the query and type parameters ('by name' and 'metro areas or states') but does not add significant new parameter-level detail beyond what the schema already states. The limit parameter is not mentioned, but it is fully documented in the 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 gives a specific verb ('Find'), a concrete resource ('real-estate markets'), and the key output ('latest typical home value / Zillow Home Value Index'). It also frames this tool as the discovery step for realestate_home_values, realestate_rents, and realestate_trend, which clearly differentiates it from those siblings.
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 explicitly tells the agent when to use this tool: 'Use this to discover the region name/id before calling' the three real-estate sibling tools. This is clear sequential guidance with named alternatives, so the agent can route correctly without opening schemas.
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.