Dubai Data — Dubai real estate statistics

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description still adds real value beyond that: output format (Markdown with figures, limits and a citation line), provenance (Dubai Land Department open data), freshness (updated daily) and a licensing/citation obligation — useful context for a consuming agent.

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?

Three sentences, front-loaded with the action and id format before provenance/citation details. Slightly dense on data-provenance trivia, but every sentence serves invocation or correct citation.

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?

With no output schema, the description enumerates the return fields (id, title, text, url, metadata), describes the body format, and tells the agent how to cite. Nothing essential for calling it correctly is missing.

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 0%, so the description must carry the id parameter, and it does: it specifies the 'type:slug' format with a concrete example ('community:dubai-marina'). That is exactly the information the bare schema lacks.

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?

States a specific verb (fetch) and resource (the full readable record for one search result by id), plus what the returned payload contains. It implies the link to the `search` sibling by saying the id comes from 'one search result', but never names `search` or explicitly contrasts itself with `get_entity`, so differentiation is clear-by-inference rather than stated.

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?

Usage is implied rather than spelled out: the reader infers you call `search` first, take an id, then call this. There is no explicit when-to-use/when-not statement or comparison against `get_entity` (which sounds like a plausible alternative for retrieving a single thing).

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?

The annotations already cover read-only, idempotent, non-destructive and closed-world behavior, and the description goes well beyond them: it discloses the data provenance (Dubai Land Department open data), daily refresh cadence, the CC BY 4.0 license, the truncation caveat on monthly price history, and the citation obligation to link the returned `url`. For a read-only statistics tool this is unusually rich behavioral context.

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?

Front-loads the core action and the slug prerequisite in the first sentence, then layers the type enumeration, returned fields and data provenance. The 'Data:' sentence is dense but every clause earns its place; it is somewhat list-heavy rather than sparse, but nothing is redundant.

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?

With no output schema, the description compensates by enumerating the returned fields (price and AED/sqft medians, YoY changes, off-plan share, bedroom breakdown, monthly price history, developer projects). Provenance and truncation are covered, but it says nothing about behavior when the slug is unknown or invalid, which an agent would want to know.

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 0%, so the description must carry the load, and it does: it defines the valid `type` values (project | community | developer | area) and explains that `slug` is an entity identifier that must first be resolved via `search`. It also notes that developer entities additionally return their projects. The only gap is any statement of slug format or case sensitivity.

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?

States a specific verb and resource ('Get the full statistics record for one project, community, developer or area by slug') and enumerates exactly which entity kinds are valid. It also names the sibling `search` tool that supplies the slug, so an agent can distinguish retrieval from discovery without opening any schema.

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?

Explicitly routes the agent to `search` to obtain the slug, which is the key prerequisite for calling this tool. It gives clear context for use, but does not say when to prefer a sibling such as get_market_summary or get_monthly_report over this one, so the boundary against those tools is left implicit.

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?

Annotations already declare readOnlyHint, idempotentHint, closed-world and non-destructive, so the safety profile is covered. The description adds real context beyond them: the data provenance (Dubai Land Department open data), the daily refresh cadence, the CC BY 4.0 license, and the requirement to link the response `url` when citing – none of which is expressible in annotations or schema.

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?

Front-loaded with the purpose in the first clause, then a dense but purposeful list of returned metrics, then provenance/citation guidance. The metric enumeration is long, but it is doing necessary work because no output schema exists; almost every element earns its place.

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?

With no input schema and no output schema, the description carries the full burden and meets it: it enumerates the returned fields, discloses the source, refresh frequency and license, and tells the agent how to cite (link `url`). An agent can call this zero-arg tool correctly and use its result responsibly from the description alone.

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?

There are zero input parameters, so by rule the baseline is 4. The description correctly adds no parameter detail; the enumerated metrics describe output fields, not inputs, so there is no parameter semantics to clarify or obscure.

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?

States a specific verb and resource ('Get the overall Dubai property market summary') and then enumerates exactly what the summary contains (sales count, median price, YoY changes, off-plan share, breakdowns, history, monthly series). The word 'overall' implicitly separates it from the sibling get_monthly_report, but no sibling is named, so the differentiation is not fully explicit.

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 makes clear this is a no-argument, whole-market aggregate, which implies it is the starting-point/overview call and that get_monthly_report or get_entity handle narrower scopes. However, it never states when to prefer this tool over its siblings or any precondition, so usage must be inferred.

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?

Annotations already declare this a read-only, idempotent, non-destructive operation, so the safety profile is covered. The description adds genuinely useful non-obvious context: the data source (Dubai Land Department open data), its daily refresh cadence, the CC BY 4.0 license, and an instruction to link the returned `url` when citing.

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 content is front-loaded (what the report contains) followed by parameter and provenance notes, in three tight lines with no filler. Every sentence contributes information the agent needs.

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?

There is no output schema, yet the description compensates by enumerating the returned fields, and it covers the single input parameter, data provenance, freshness, and licensing. An agent has everything needed to invoke it correctly and cite the result.

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 0%, so the description carries the full burden for the single `month` parameter, and it does so: it gives the expected format ('YYYY-MM') and states that omitting it returns the latest report, which matches the null default in 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 names a specific verb and resource ('Get the monthly Dubai property market report') and enumerates the report contents (sales, median price, AED/sqft, YoY, top movers, citation string), so an agent knows exactly what it returns. It does not explicitly contrast itself with the sibling get_market_summary, so the differentiation is implied rather than stated.

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?

Usage is implied by the tool name and the 'omit for the latest report' note, which tells the agent the default case. There is no explicit when-to-use guidance and no mention of when a sibling such as get_market_summary or list_rankings would be the better choice.

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?

Annotations already declare read-only, idempotent, non-destructive behavior, so the description focuses on what annotations cannot convey: the data source (Dubai Land Department open data), daily refresh cadence, licensing (CC BY 4.0) and the citation obligation via the row `url`. Pagination/response-size behavior is left unstated, keeping it out of 5 territory.

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?

Front-loaded with the core action, then compact parenthetical definitions per kind, with data provenance and citation pushed to the end. Dense but every clause adds information; the kind list is long but not redundant.

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?

With no output schema, the description compensates by stating that each row includes a `url` and by naming the metrics returned per kind. Provenance and citation requirements round it out. Remaining gaps (ordering semantics, tie-breaking, response envelope) are 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?

Schema coverage is 0% and the `kind` parameter has no enum, so the description carries the full burden — and it does, defining all four valid values and their units (AED, %, AED/sqft, sales counts). It also supplies the `limit` range (1-100) and default context missing from 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?

States a specific verb (List) and resource (top rankings), then enumerates the four kind values with their precise measures, so an agent knows exactly what each call returns. It does not reference any sibling tool to differentiate itself, so it stops short of a 5.

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 kind descriptions imply when each ranking is appropriate (e.g. rents vs yields vs sales volume), which is useful selection guidance. However, there is no explicit when-to-use vs when-not guidance and no mention of alternatives like get_market_summary, leaving routing to inference.

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?

Annotations already cover read-only, idempotent, closed-world safety. The description adds genuine context beyond that: deterministic ranking (exact > prefix > contains, then sales count), daily data freshness, CC BY 4.0 licensing, and the null-url edge case for projects without a page.

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?

Front-loaded with purpose and matching rules, then parameters, then return shape and provenance. Dense but each sentence carries distinct information; slightly long but no filler.

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?

With no output schema, the description compensates by enumerating return fields (id, title, type, name, slug, url, and key figures) and the id-to-fetch handoff. Only the `limit` parameter's behavior remains unaddressed.

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 0%, so the description must carry the load. It fully covers `query` matching rules and the `type` enum values (project|community|developer|area), but says nothing about `limit`, leaving one of three parameters undocumented anywhere.

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?

States a specific verb (search) and resource (Dubai projects, communities, developers, areas) with the searchable field (name) and matching semantics. An agent can distinguish it from fetch/get_entity, which retrieve a single known entity rather than query by name.

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?

Explicitly routes the agent to the sibling `fetch` by noting the returned `id` should be passed to it, which clarifies the search-then-fetch workflow. It does not state when to prefer this over get_entity or list_rankings, so exclusions are absent.

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