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property_lookup

Read-onlyIdempotent

Look up real estate property data by street address or account number. Returns assessed value, market value, land value, improvement value, year built, square footage, lot size, acreage, exemptions (homestead, over 65, disabled veteran), and legal description. Use this for questions like "how much is this property worth?", "what's the tax value of this address?", "what are the property details?", or any real estate lookup. Owner names, heirs, deed opinions, and title conclusions are not returned. Coverage note: currently demo dataset for Montgomery County, TX (sample properties only). Broader live county coverage is not enabled by default and must be confirmed before purchase. Email support@livedatalink.ai to discuss a source-verified coverage requirement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesStreet address or appraisal district account number
countyNoCounty name (default: montgomery)montgomery

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description adds substantial behavioral context: the exact data fields returned, explicit non-returned categories, and a sharp coverage limitation that the data is only a Montgomery County, TX demo dataset with sample properties. This goes well beyond what the annotations alone provide.

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 front-loaded with the core purpose and return fields, and the exclusions and coverage note are clearly placed. It is slightly longer than needed due to the example question list and the phrase 'any real estate lookup,' which is a bit overbroad, but it remains organized and readable.

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

Completeness5/5

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

There is no output schema, but the description compensates by enumerating all key return fields and explicitly stating what is not returned. It covers input types, coverage limitations, default county behavior, and a contact path for broader coverage. This is complete enough for an agent to call the tool and interpret results correctly.

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

Parameters4/5

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

The schema already documents both parameters at 100% coverage, so the baseline is 3. The description adds meaningful context by clarifying that broader county coverage is not enabled by default, which directly affects how the county parameter should be used, and reinforces that query accepts either an address or an account number.

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: 'Look up real estate property data by street address or account number.' It lists the exact return fields and clarifies what is not returned, which helps distinguish it from sibling tools like property_search_owner or title-oriented tools.

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 and a clear 'Use this for' statement. It also gives important exclusions and a coverage note. However, it does not name any alternative sibling tool for owner or title searches, so the routing guidance stops short of being fully explicit about alternatives.

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.

Resources