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LiveDataLink

by io.github.blackboxfoundry

price_to_rent_ratio

Read-onlyIdempotent

Compute the buy-vs-rent price-to-rent ratio for a US metro or state: Zillow home value (ZHVI) divided by annualized rent (ZORI x 12). Returns the ratio plus a rule-of-thumb signal (below ~15 favors buying, above ~20 favors renting). Premium synthesis over the real-estate market store. Rule-of-thumb only, not financial advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionYesMetro or state name/id (e.g. 'Austin, TX', 'California', or a Zillow region id).
region_typeNoOptional region type filter: 'metro' or 'state'.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only and idempotent behavior, so the bar is lower. The description adds meaningful behavioral context: it returns both a numeric ratio and a rule-of-thumb signal, includes interpretative thresholds, and explicitly labels itself as not financial advice. This goes beyond what annotations 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 compact and front-loaded: the main computation and scope appear first, followed by the output shape and an important caveat. The phrase 'premium synthesis' is somewhat vague filler, but it does not undermine overall efficiency.

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?

For a simple two-parameter, read-only computation tool with full schema coverage, the description is complete. It explains the formula, the input scope, the returned values, and the interpretive signal. The absence of an output schema is adequately compensated by describing what the tool returns.

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?

Schema description coverage is 100%, so the schema already documents both parameters well with examples. The description reinforces that 'region' is a US metro or state and contributes formula context, but it does not need to add parameter-level detail beyond what the schema provides.

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 clearly states the specific operation: computing the price-to-rent ratio for a US metro or state via a named formula (ZHVI / (ZORI x 12)). It distinguishes itself from sibling real-estate tools like realestate_home_values and realestate_rents by framing it as a synthesized buy-vs-rent metric, not raw data retrieval.

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 clear context for when this tool is appropriate: when a combined buy-vs-rent ratio and signal is needed rather than raw home values or rents. It doesn't explicitly name alternatives or exclusions, but the formula and 'synthesis' wording make the intended use clear relative to the sibling tools.

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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