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property_search_area

Read-onlyIdempotent

Search for real estate properties in a geographic area. Filter by zip code, subdivision, neighborhood, or street name. Use this for questions like "what homes are in this zip code?", "show me properties in this neighborhood", "find houses on Main Street", "what's the average home value in this area?", or any area-based property search. Returns a list of matching records with addresses, values, and property types; owner names are not returned. Coverage note: the current Montgomery County, TX connector returns demo/sample records only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipNo5-digit ZIP code to search within
countyNoCounty name (default: montgomery)montgomery
streetNoStreet name to search (e.g., 'Main St')
subdivisionNoSubdivision or neighborhood name

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: it discloses that owner names are absent, that the current Montgomery County connector returns demo/sample records only, and that results include addresses, values, and property types. This is especially important because there is no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: function statement, filter options, usage examples, return shape, and a critical data caveat are all present in a compact form. The example questions are slightly verbose but serve a real routing purpose, so each sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema, the description reasonably covers return contents and a data-quality caveat. It does not specify whether at least one filter is required or how multiple filters combine, but the examples and filter list give an agent enough context to invoke the tool successfully. It is broadly complete for a read-only area search.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% coverage for all four parameters, each with a clear description. The tool description echoes the filter dimensions (zip, subdivision, neighborhood, street) but adds little new meaning beyond what the schema states. Baseline 3 is appropriate because the schema carries the semantic weight.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Search for real estate properties in a geographic area.' It clearly differentiates itself from owner-based or record-lookup siblings by emphasizing area-based searching and explicitly stating that owner names are not returned. This makes the tool's purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit example questions that signal when to use the tool, such as 'what homes are in this zip code?' and 'show me properties in this neighborhood.' It does not explicitly name alternative tools or state when not to use it, but the usage context is clear enough for an agent to route area-based property queries here.

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

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TDQS

B3.2/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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