Reevl — Korean Real Estate

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description adds context beyond what is already structured. It details the returned categories and the practical preflight caution about units, which gives the agent a meaningful behavioral expectation.

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 compact and front-loaded with the core purpose. The unit-error example is concrete and earns its place by reinforcing the recommended preflight usage without unnecessary verbosity.

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 sufficiently names the outputs that matter: axes, units, distribution stats, and sido-level counts. The optional narrowing via 'sido' and 'field' is covered in the schema, so the whole preflight workflow is clear, even though the exact response format remains open.

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 both optional parameters are already described clearly. The prose description does not repeat or add parameter semantics, but the baseline of 3 is appropriate because the schema carries the load and the description does not conflict.

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 states a specific verb and resource: it provides all search axes, units, value distribution, and counts by sido. This clearly differentiates it from search/list/get tools because it is a metadata/preflight tool rather than a data retrieval tool.

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 explicitly says to call it once before condition search and explains why missing unit information can cause a 100x error. This is clear when-to-use guidance, and the alternative search tools are implicit as later steps, though there is no explicit when-not-to-use clause.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds context about returning a bundled set of axes and depending on a search-selected cid, but it does not describe the response shape, pagination, or error behavior.

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 short sentences: what it returns, how to obtain its input, and when to use it. Every sentence earns its place; there is no redundancy or 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?

For a tool with a single required parameter and no output schema, the description adequately explains the output categories, the input source, and the intended use case. A fully explicit return contract would be better, but the category list and workflow guidance make the tool callable without further inference.

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 schema already fully documents cid with a concrete example, meeting the high-coverage baseline. The description adds the important bit of input provenance: the cid must come from search_apartments, which helps the agent supply a correct value rather than an arbitrary one.

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 states a specific action and resource: get a single complex's detail as a cohesive bundle, with concrete categories enumerated. It also distinguishes itself from search_apartments by framing this as the follow-up detail call for a selected cid.

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?

It explicitly says to call it with the cid obtained from search_apartments and defines when it is appropriate ('when you need to judge a specific complex'). It does not explicitly contrast it with list_articles or describe_fields, but the workflow guidance is clear.

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 communicate read-only, idempotent, and non-destructive behavior, lowering the burden on the description. The description adds useful context about the article frequency and the content of returned items, but does not reveal further behavioral details such as ordering, pagination semantics, or update latency. Beyond the annotations, the added value is moderate.

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, each carrying essential information: the first defines what the tool lists and with which key fields, the second gives a reason for using the tool along with its update frequency. There is no repetition of schema/annotations and 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?

For a single-optional-parameter tool with no output schema, the description covers the key aspects: what it returns, general content, and when to apply it. It could additionally mention whether the list is ordered by publication date or how recent the articles are, but the described use case and fields make a call scenario complete enough.

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 already fully describes the only parameter (limit: 1~30, default 10), resulting in 100% schema coverage. The description does not mention the limit parameter or add additional semantics, so it does not improve on the schema; per the baseline, this is a 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 states a specific verb ('목록' – list) and resource ('리블 아티클' – Rible articles), and enumerates the typical fields returned: title, summary, publication date, link. This unambiguously differentiates it from sibling tools focused on data fields or apartment search, so an agent can identify when to use it.

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 explicitly gives a usage context: use as evidence for recent market situations or policy flows, and notes that the articles are published almost daily. It does not mention alternatives or exclusions, but the tool's purpose is distinct enough from its siblings that a separate differentiation is not necessary.

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?

Beyond annotations that already declare readOnly and idempotent hints, the description adds material behavioral guidance: the response includes 'units' and 'total', the agent must read units rather than invent them, numeric filtering uses <axis>_min/<axis>_max, values such as 60 mean 60%, and 20 results are returned by default. This meaningfully compensates for the absence of an output schema and protects against common invocation mistakes.

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 compact and well structured, front-loading the main function and sample result types. Every sentence adds decision-relevant context: when to use, syntax orientation, response semantics, and default result size. There is no filler or duplication of schema entries.

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 is complex with 17 optional parameters and no output schema, but the description covers the critical usage surface: condition-style questions, exact-match vs range behavior, response units warning, total count, and default limit. Combined with the highly detailed parameter-level schema descriptions, this is enough for an agent to invoke the tool correctly without guessing.

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?

Despite 100% schema coverage, the description adds a reusable semantic layer across parameters: numeric axes follow a shared <axis>_min/<axis>_max ranged notation, values such as percentages are used literally without conversion, and some fields are exactly matched while others are range-based. This context explains the parameter convention better than the individual field descriptions 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 states the tool's job clearly: filtering over about 45,000 nationwide apartments based on conditions, with concrete example queries such as '강남구 20억 이하 대단지'. It also distinguishes itself as the condition-search entry point from the sibling tools by focusing on filtering, though it does not explicitly name or contrast siblings.

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 explicitly says when to use the tool: for questions that carry filtering conditions, and gives realistic query patterns. It gives useful constraints such as exact matching for region/brand/builder and min/max syntax for numeric axes, but it does not explicitly tell when not to use it or which sibling alternative to prefer.

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