Compare 2-5 Dubai areas side by side: median sale price, price per m2, and gross rental yield.
| Name | Required | Description | Default |
|---|---|---|---|
| areas | Yes | List of 2-5 area names in English, e.g. ['Business Bay','Dubai Marina']. | |
| property_type | No | 'Flat' (default) or 'Villa'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses the metrics compared but does not mention whether the tool is read-only, data freshness, or any side effects. Missing behavioral details beyond the core operation.
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?
Single sentence that is front-loaded with the core purpose. Every word adds value, no redundancy.
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?
The description explains the purpose and the metrics, which is sufficient given the two simple parameters. However, without an output schema, it lacks information about the return format (e.g., table, JSON). Still, it is fairly complete for a low-complexity tool.
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 coverage is 100% with descriptions, but the tool description adds valuable context like the example format and the specific metrics compared (median sale price, price per m2, yield). This goes beyond the parameter descriptions, justifying a score above the baseline 3.
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 it compares 2-5 Dubai areas on specific metrics (median sale price, price per m2, gross rental yield). It is a specific verb+resource and distinguishes from siblings like 'area_snapshot' which focuses on single areas.
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 usage for comparing multiple areas side-by-side, but does not explicitly state when to use vs alternatives like 'market_overview' or 'affordability'. No exclusions or prerequisites are mentioned.
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.