コエカツ アンケート集計データ

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

With no annotations, the description carries the burden of disclosing behavior. It indicates this is a read operation ('取得') and notes the nullable citation.stable_url condition. However, it does not disclose potential side effects, authentication requirements, or other behavioral nuances beyond the basic retrieval.

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, consisting of two sentences. It front-loads the core purpose and adds essential return-value details without unnecessary fluff.

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 adequately explains the tool's primary function and return fields, including edge cases (null citation URL). While no output schema exists, the description lists all key return components, making it sufficiently complete for a simple retrieval 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?

The schema already covers 100% of parameters with descriptions, so the baseline is 3. The description adds no parameter-specific details, but given the high schema coverage, it doesn't need to compensate.

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 specifies the action ('取得します' - retrieves) and the exact resource (one survey's questions, choices, vote counts, percentages, plus citation URL and license). This differentiates it from sibling tools like search_surveys (which searches) and list_categories (which lists categories).

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 is provided in the description about when to use this tool versus search_surveys or list_categories. The need for a survey_id is implied but not explicitly connected to search_surveys, and no exclusions or alternative workflows 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?

No annotations are provided, so the description carries the full burden of explaining behavior. It adds useful context about the hierarchical view and the read-only act of confirming categories, but it does not state the return format, potential side effects, or permissions. For a simple list tool, this is minimally adequate.

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, front-loaded with what the tool does, followed by when to use it. Every phrase earns its place, with 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 one optional parameter and no output schema, the description covers the core purpose and a typical use case reasonably well. It would benefit from mentioning the response shape (e.g., category IDs and names), but the low complexity makes the current description largely complete.

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 fully describes parent_id with 100% coverage, so the baseline is 3. The description's mention of the hierarchy adds helpful context for understanding parent-child relationships, but it does not add parameter-specific details beyond what the schema already 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 what the tool does: it lets you view research fields organized in a hierarchy of major, middle, and minor categories. It uses a specific verb ('確認できます') and resource ('カテゴリ'), and it is distinct from the sibling tools by focusing on category browsing rather than survey retrieval or search.

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 gives a clear use case: 'use it when you want to understand what can be investigated.' It does not explicitly mention alternative tools or provide when-not-to-use guidance, but the context is sufficiently clear for a simple listing tool.

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 burden and does disclose a useful behavioral trait: paraphrase matching (e.g., AI/artificial intelligence). It also indicates the output's role, but does not detail the return structure, pagination, or search scoping, leaving some transparency gaps.

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 concise sentences: the first states the core action, and the second adds valuable behavioral and workflow context. It is front-loaded, free of filler, and every sentence 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?

Given the absence of an output schema, the description adequately explains the key workflow: search returns survey_id which is passed to get_survey. It also covers paraphrase matching, but omits return format details such as whether a list is returned or how results are ordered, leaving minor gaps.

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 100% coverage with descriptive text for all four parameters. The description's mention of keyword search and natural phrasing closely mirrors the schema's query description, adding no new parameter-level meaning beyond what the schema already 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's function: 'search surveys by keyword' with a specific verb and resource. It distinguishes itself from siblings by explicitly noting returned survey_id feeds into get_survey, and the paraphrase-matching detail adds further scope clarity.

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 when to use this tool (when you need to find surveys to obtain survey_id) and explicitly outlines the workflow to get_survey. It lacks explicit 'when not to use' guidance or naming of alternative tools, but the context is clear.

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