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

No annotations provided, so description carries full burden. It clearly describes behavior: returns category summary without parameter, full API list with parameter. No destructive or hidden side effects implied. Lacks mention of rate limits or authentication, but not critical for a guide 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?

Four short, focused sentences. First sentence states main function. Second and third detail parameter behavior. Last gives usage order. No redundant information. Perfectly front-loaded.

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 guide tool with no output schema and one optional parameter, description fully covers what it returns in both cases and how it fits with siblings. No gaps given the tool's simplicity.

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 already has 100% coverage with enum values and description. Description adds context: without category, returns summary; with category, returns API list. This adds meaningful usage context beyond 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?

Description clearly states that the tool lists 157 OpenAPI items categorized, and specifies behavior with and without the category parameter. It distinguishes from siblings by recommending a usage order (fiscal_guide → fiscal_search → fiscal_query), showing unique role as a guide.

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 states when to use (category summary or full API list) and not to use (data search/fetch, which are for fiscal_search and fiscal_query). Provides a clear ordering recommendation, helping AI agent decide correctly.

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 bears full responsibility for behavioral disclosure. It mentions default page_size and max, but fails to describe whether the tool is read-only, the nature of the API call (e.g., HTTP GET vs POST), error handling, or rate limits. For a data query tool, this is insufficient.

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 concise (three sentences), front-loads the main purpose, and uses bullet points effectively. However, the first line could be slightly more specific about the kind of data (e.g., fiscal data). Overall efficient.

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 has 4 parameters, 1 required, and no output schema, the description adequately covers the basic usage. But it lacks details about return structure, error scenarios, and pagination behavior beyond page_size. It meets minimum viability but is not comprehensive.

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 the baseline is 3. The description adds no additional parameter information aside from the default page_size, which is already present in the schema. It adds minimal value 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 states the tool queries the '열린재정' API to retrieve data. It specifies the action (querying), resource (API), and distinguishes from siblings by mentioning that the api_code should be checked via fiscal_search or fiscal_guide, implying it is a general-purpose query 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 provides explicit guidance: users must first use fiscal_search or fiscal_guide to obtain the api_code. It also notes the default page_size of 100 (max 1000). However, it does not specify when not to use this tool or provide alternatives for other scenarios.

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 description carries burden. It mentions the search returns results with 'code' but does not specify response structure, pagination, or error handling. However, it provides enough context for agent to understand it's a search/discovery step.

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, no fluff. Front-loaded with purpose, then usage instruction, then concrete example. Every sentence adds value.

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 and no annotations, the description is self-contained for a search tool that feeds into a sibling tool. The example bridges the gap, making the workflow complete. No return type explanation needed as it's meant to be interim.

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 description enriches parameters with examples (keyword: '국가채무') and explicit enum values with translations. The category descriptions and enum values are well explained in schema, but description adds workflow context.

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 it searches '열린재정 API' by keyword or category, specifying the resource and action. It differentiates from siblings by instructing to check 'code' and then call fiscal_query for data retrieval.

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 states when to use (to search and find a code) and when to use fiscal_query instead (to fetch data with the code). Provides an example workflow with fiscal_search → code → fiscal_query.

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