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AVnester — Indian Real Estate Intelligence

Get one AVnester property by ID

get_property_details
Read-onlyIdempotent

Fetch full public details for one AVnester listing by its listingId. Get the listingId from search_properties results first (the listingId field) — IDs are not guessable, so this tool is the natural follow-up to a search. AVnester's catalog covers Coimbatore and Chennai; unknown or unpublished IDs return { listing: null, notFound: true } (never throws, to avoid leaking existence). Use when the user references a specific listing. Read-only, no side effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
listingIdYesAVnester listingId, as returned in search_properties results (the `listingId` field). IDs are not guessable — call search_properties first. Also the trailing segment of a listing sourceUrl.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cardsYes
trustNo
listingYes
messageNo
notFoundYes
disclaimerYes
handoffUrlNo
attributionYes

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description discloses important runtime behavior: unknown/unpublished IDs return { listing: null, notFound: true } without throwing, purposely to avoid leaking existence. It also notes catalog coverage and that IDs are not guessable, which adds significant context beyond the structured annotations.

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 compact (three sentences) and front-loaded with the core purpose. Every sentence adds value: the first states the main function, the second gives prerequisite and sequence, the third explains edge-case behavior and scope. No wasted words.

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 the tool's simplicity (one parameter), the rich schema and annotations, and the presence of an output schema, the description is complete. It covers prerequisites, manual fallback (sourceUrl), geographic scope, and not-found behavior. Nothing important is missing.

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 input schema already provides comprehensive parameter semantics with 100% coverage: it explains listingId is from search_properties, not guessable, and the trailing segment of sourceUrl. The description repeats this guidance but adds no new parameter-level information beyond what the schema already documents, so the baseline of 3 is appropriate.

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 tool fetches full public details for one AVnester listing by listingId. It uses a specific verb ('Fetch') and resource ('AVnester listing'), and distinguishes itself from siblings by being the follow-up to search_properties for a single listing.

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 gives explicit usage context: get listingId from search_properties first, and use when the user references a specific listing. It also notes catalog coverage (Coimbatore and Chennai). However, it does not explicitly name alternatives for other use cases (e.g., compare_properties for multiple listings), so it lacks a full 'when-not or alternative' statement.

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

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: search, details, comparisons (localities vs. listings), and specific financial calculations (EMI, stamp duty, tax, prepayment, balance transfer, decision intelligence). Descriptions clearly differentiate between similar-sounding tools like calculate_home_affordability and get_property_decision_intelligence.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., compare_properties, get_locality_insights, simulate_loan_prepayment). The verbs are descriptive and uniform in style, with no mixed conventions or vague names.

Tool Count5/5

11 tools is well-scoped for a real estate intelligence server, covering property search, locality analytics, and financial calculators. Each tool has a clear purpose and the set feels neither sparse nor bloated.

Completeness5/5

The tool surface covers the full home-buying journey in India: searching properties, getting details, comparing localities/listings, estimating stamp duty and loan payments, optimizing tax regime, simulating prepayment, and assessing overall affordability with decision intelligence. No obvious gaps or dead ends.

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