dxb-data

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 core behavior (listing areas by median sale price fitting budget, sorted descending), but lacks details on edge cases like no matching areas or handling of invalid input. It also does not mention the data source or any side effects.

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?

The description is a single, well-structured sentence that is front-loaded with the condition and action. No unnecessary words or repetition.

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 the simplicity of the tool and lack of output schema, the description adequately explains input and high-level output. However, it does not specify what information is shown for each area (e.g., price, name only) or the exact ordering logic, which could be clarified.

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%, so baseline is 3. The description adds context by stating the budget is in AED and that property_type defaults to 'Flat', but these are already in the schema descriptions. No additional semantic enrichment beyond what the schema provides.

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 identifies the tool's purpose: given a budget in AED, list Dubai areas where the median sale price fits, ordered by largest affordable first. This verb-resource pairing is specific and distinct from sibling tools like compare_areas or market_overview.

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 when a user wants to find affordable areas within a budget, but it does not explicitly state when not to use it or provide direct alternatives. The sibling tools are listed externally but not referenced.

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

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.

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.

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

With no annotations, the description must convey behavior. It says 'List all...available,' implying a safe read operation, but omits details like response format or pagination. Adequate but minimal.

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?

Two sentences with no wasted words. The action ('List all Dubai areas') is front-loaded, and the usage hint is concise.

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?

For a parameterless listing tool with no output schema, the description sufficiently covers purpose and usage. It could mention the output is area names, but the dataset context implies it.

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?

The input schema has zero parameters with 100% coverage. The description adds value by explaining the tool's purpose (discovering valid area names) beyond the schema.

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 'List all Dubai areas (neighborhoods) available in the dataset,' using a specific verb and resource. It inherently distinguishes from sibling tools that focus on metrics like affordability or 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?

States 'Use to discover valid area names,' providing a clear intended use case for seeding other area-specific tools. However, it does not explicitly exclude alternatives or mention when not to use.

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

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

With no annotations provided, the description carries the full burden. It clearly describes the output content (transactions, areas, median price, top areas) and implies the tool is read-only with no side effects. However, it does not specify data recency or any permissions needed.

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?

The description is a single, compact sentence that efficiently communicates the tool's purpose and output. It is front-loaded with 'Overall Dubai real estate snapshot' and lists key components with no wasted words.

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 the tool's simplicity (no parameters, no output schema), the description is sufficiently complete. It covers the main output aspects. A small gap is that it does not specify whether the date range is fixed or user-selectable, but since there are no parameters, this is minor.

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?

The input schema has no parameters (0 params), so baseline is 4. The description adds value by explaining what the tool returns, which is more informative than the empty schema alone.

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 an 'Overall Dubai real estate snapshot' and lists specific included metrics (total transactions, areas, date range, median sale price, top 5 areas). This distinguishes it from sibling tools like area_snapshot which focus on specific 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 a global market overview, but lacks explicit guidance on when to use this tool versus alternatives (e.g., when to use area_snapshot instead). No when-not-to-use or alternative tool names are mentioned.

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

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

With no annotations provided, the description must fully disclose behavioral traits. It mentions data source ('official DLD sale + rental data') and ordering ('highest first'), but lacks details on data freshness, caching, authentication requirements, or any side effects. Basic transparency is present but insufficient for a 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.

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

The description is two sentences long with no unnecessary words. It efficiently conveys purpose, data source, and use case. Every sentence adds value, making it highly concise and well-structured.

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 tool has no output schema and no annotations, so the description should compensate by detailing the return structure. It does not mention what fields (e.g., area name, yield value) are returned, whether results are paginated, or how to interpret the output. This leaves significant gaps for an agent invoking the 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%, and both parameters are clearly described in the input schema. The description does not add additional meaning beyond what the schema provides (e.g., default and max values for 'limit', valid values for 'property_type'). Baseline 3 is appropriate since schema already handles parameter semantics.

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 verb 'rank', the resource 'Dubai areas', and the metric 'gross rental yield (highest first)'. It also provides context about data source ('official DLD sale + rental data') and use case ('Great for finding the best-yielding neighbourhoods'). This distinguishes it from sibling tools like 'area_snapshot' or 'compare_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 ('Great for finding the best-yielding neighbourhoods') but does not provide explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or conditions for using this tool versus siblings like 'rental_yield' or 'compare_areas'.

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

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

No annotations provided. Description mentions data source (DLD and Ejari) implying reliability but does not disclose read-only nature, rate limits, error cases, or update frequency. Basic transparency for a read 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, 27 words, front-loaded with purpose and outputs. No extraneous 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?

No output schema, but description explains return values. Missing details on error handling (invalid area) and default behavior for property_type. Data source is included. Adequate for a simple query 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%, baseline 3. Description adds context about area being in Dubai and outputs (rent, sale price, yield) beyond the schema descriptions. Property_type default is mentioned in schema, description reinforces residential focus.

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?

Description clearly states the tool computes gross rental yield for a Dubai residential area, specifying the exact outputs (median rent per m2, median sale price per m2, yield %) and data source. It distinguishes from siblings by focusing specifically on yield calculation.

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?

No guidance on when to use this tool versus siblings like area_snapshot, market_overview, or rank_areas_by_yield. The description does not mention prerequisites or context for choosing this tool.

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