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PropContext — Bangalore Real Estate

by app.railway.up.propcontext-production

get_project_insights

Read-only

Get AI-computed market insights for a specific Bangalore project.

Returns three computed signals derived from 2,700+ RERA projects:
- Price intelligence: how this project's price/sqft (or starting price) compares to the locality median
- Builder scorecard: track record — total projects, completed, lapsed, avg construction progress, verdict
- Sales velocity: % units booked at this project vs the locality average

Use this when the user asks:
- "Is this fairly priced?" / "Is this good value?"
- "Can I trust this builder?" / "What's the builder's track record?"
- "Is this project selling well?" / "How popular is this project?"
- Wants a data-driven assessment before booking a visit

Requires a RERA number from search results. Call search_projects or get_project first if needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rera_numberYes

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context: the tool is AI-computed, derives from 2,700+ RERA projects, and returns three named market signals. This goes beyond the annotations without contradicting them.

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 organized: a one-sentence purpose, a bulleted list of output signals, trigger questions, and a prerequisite note. Every section earns its place, and there is no redundant or filler content.

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?

Despite having no output schema, the description thoroughly explains what the tool returns by listing all three signals and their meaning. It also covers usage triggers, prerequisites, and data source, making it complete for an agent to select and invoke the tool 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?

With 0% schema description coverage, the description compensates by explaining that rera_number must come from search results, not just any string. It also provides the prerequisite pipeline for obtaining that value. It does not give a RERA number format example, but the single required parameter is adequately grounded.

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 names a specific verb and resource: 'Get AI-computed market insights for a specific Bangalore project', then details the three computed signals. This clearly distinguishes it from sibling tools like get_builder_projects or search_projects, since it is project-specific and insight-focused.

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 when-to-use triggers ('Use this when the user asks:') with concrete examples, and states the prerequisite that a RERA number is needed, directing the agent to call search_projects or get_project first. It stops short of enumerating when-not-to-use cases or naming all sibling alternatives, but the usage context is clear.

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

All PropContext — Bangalore Real Estate toolsInspect
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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct jobs: search vs. details vs. insights vs. booking/callback. The only mild overlap is among the three search tools (structured, fulltext, commute) and between get_project_insights and get_builder_projects on builder credibility, but the descriptions are strong enough to guide correct selection.

Naming Consistency5/5

All tool names follow a predictable verb_noun snake_case pattern: search_* for discovery, get_* for retrieval, and book_/request_ for lead capture. There are no mixed conventions, vague verbs, or inconsistent casing.

Tool Count5/5

10 tools is well-scoped for a real estate assistant: three discovery modes, project detail and insights, builder verification, new launches, personalized shortlist, and two lead-capture actions. Each tool has a clear purpose and the set never feels bloated or thin.

Completeness5/5

The tool surface covers the full buyer journey from discovery and verification to personalized recommendations and conversion (site visit or callback). It also includes builder track-record checks and recent registrations, so there are no obvious dead ends for common real estate queries.

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