DealScanner

top_investment_areas

Rank the best areas to invest (neighborhoods + municipalities).

Answers "where should I invest?" using DealScanner's market intelligence.

Args:
    sort_by: Ranking metric - one of "cap_rate" (avg cap rate),
        "roi"/"flip_watch" (projected flip ROI), "count" (inventory),
        or "price" (lowest median price first).
    limit: Number of areas to return (1-50).

Returns a dict with `areas` (name, type, cap_rate, roi, count,
median_price, ...) sorted by the chosen metric.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`sort_by`	No		cap_rate

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It transparently explains the sorting metrics, result format, and output fields, strongly implying a read-only query operation. Minor gaps like authentication or data freshness are not critical for this ranking tool.

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

Conciseness5/5

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

The description is well structured and compact, front-loading the purpose and then using Args/Returns sections to document parameters and output. Every sentence contributes actionable information without redundancy.

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?

Given no annotations and no output schema, the description fully compensates by covering the tool's purpose, parameter semantics, and expected return structure. An agent has enough information to invoke the tool correctly and interpret its results.

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

Parameters5/5

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

The schema has no descriptions, but the description fully covers both parameters: it lists all allowed values for sort_by with their meanings (including aliases) and the valid range for limit. This provides major value beyond the bare schema.

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 uses a specific verb ('Rank'), resource ('areas'), and scope ('neighborhoods + municipalities'), and directly answers a concrete user question ('where should I invest?'). It clearly differentiates from siblings by emphasizing market intelligence ranking rather than individual property analysis.

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 by stating it answers 'where should I invest?' using DealScanner's market intelligence. It does not explicitly name alternatives or mention when not to use it, but the intended use case is clear enough for selection.

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

A3.9/5.0

Disambiguation3/5

Most tools are clearly distinct, but analyze_brrrr overlaps with get_property when given an address and strategy=brrrr, since both can return a BRRRR analysis. The ad-hoc input option for analyze_brrrr and the broader strategy support in get_property help clarify, yet the boundary is not fully crisp.

Naming Consistency4/5

Tool names are mostly snake_case with a verb_object pattern (analyze_brrrr, get_comps, get_property, search_deals), but area_stats and top_investment_areas are noun phrases, and whoami is a standalone command. These minor deviations are easy to predict and remember.

Tool Count5/5

With 7 tools, the server is well-scoped for a real estate deal analysis domain. Each tool covers a distinct high-level task (search, analysis, comps, stats, ranking, identity) without unnecessary redundancy or excessive fragmentation.

Completeness4/5

The surface covers the core lifecycle: searching deals, analyzing properties (flip, rental, brrrr), pulling comps, getting area stats, and ranking areas. A minor gap is the lack of a dedicated 'analyze_flip' or 'analyze_rental' tool, but get_property with strategy handles those cases, so no dead ends.

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