Price snapshot for residential property in a Dubai area from official DLD sale transactions: sample size, median sale price (AED), median price per m2, date range.
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | Area name in English, e.g. 'Business Bay'. | |
| property_type | No | 'Flat' (default) or 'Villa'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the data source (official DLD sale transactions) and outputs (sample size, medians, date range), indicating a read-only safe operation. However, it does not explicitly state that no data is modified or mention rate limits or auth needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that immediately conveys the core purpose and key output fields. No redundant words or unnecessary details. Front-loaded with the most important information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains what the tool returns (sample size, median prices, date range). For a simple snapshot tool with only two parameters, this is sufficient. However, it could mention the data update frequency or geographic scope for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds context ('residential property', 'official DLD transactions') but does not elaborate on parameter constraints or default behavior beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a price snapshot for residential property in a Dubai area, specifying sample size, median sale price, median price per m2, and date range. It uses a specific verb-resource combination ('Price snapshot for area') that distinguishes it from siblings like affordability or rental_yield.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used to get statistics for a specific area, but it does not explicitly state when to use it compared to alternatives like market_overview or compare_areas. No exclusions or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: listing areas, getting area snapshot, comparing areas, market overview, affordability calculation, yield ranking, and rental yield. No overlap or ambiguity.
All tool names follow a consistent pattern of lowercase snake_case with verbs like 'list', 'compare', 'rank', 'get' (implied in area_snapshot). Very predictable.
Seven tools are well-scoped for a Dubai real estate data server. Each tool adds distinct value without redundancy, fitting the ideal range of 3-15.
The set covers essential operations: listing areas, detailed snapshots, comparisons, market overview, affordability filtering, and yield analysis. No obvious gaps for the domain.