真人意見站

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 does disclose the output shape (0–1 分數) and an important behavioral caveat that the result is advisory only ('僅供參考'), which is genuinely useful. It stops short of stating accuracy limits, language assumptions, or input-size constraints.

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 compact sentences with the purpose and the inspected signals front-loaded, followed by the score range and the advisory caveat. Nothing is padded, though the parenthetical signal list is dense for a short description.

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-parameter analysis tool with no output schema, the description covers input intent, the analysis dimensions, the return range, and the advisory caveat. That is nearly enough for an agent to call it correctly, missing only input format/length caveats.

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 is one parameter ('text') with 0% schema description coverage, and the description only implies it is the text being analyzed. The description explains what the tool looks for in that text, which adds some meaning, but gives no format, length, or language guidance for the input.

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 (檢查) and resource (一段文字的 AI 生成特徵) and even enumerates the signals inspected (常用語、排版、個人經驗用語、句長一致性). That is well above a vague purpose statement, though it does not contrast itself with the sibling tools.

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?

There is no statement of when to reach for this tool versus the siblings (get_taiwan_post, search_human_opinions, etc.), nor any exclusions. '僅供參考' is an interpretive caveat about the result, not usage guidance for selecting or applying the 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?

No annotations are provided, so the description carries the full burden. It does disclose the shape of the payload (full text plus replies), which is genuine behavioral value, but says nothing about permissions, errors for invalid ids, or whether replies are paginated.

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 short sentence that front-loads the returned content. It is efficient and free of padding, though it is arguably too terse to cover the missing parameter and usage context.

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 one-parameter read tool with no output schema, the description conveys the essential return content. However, with zero schema coverage and no annotations, it leaves the id semantics and access prerequisites unspecified, which is a meaningful gap for an agent that must construct the call.

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% and the single required parameter 'id' is not explained anywhere. The description never states what kind of id is expected (numeric post id, slug, etc.) or where an agent would obtain one, so it does not compensate for the coverage gap.

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: fetching a Taiwan real-person post's full text and its replies. It is distinguishable from the sibling list_taiwan_posts, since this retrieves a single post's body plus comments rather than enumerating posts. It stops short of 5 only because it doesn't explicitly contrast itself with the sibling 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?

There is no statement of when to use this tool versus check_ai_text, get_topic_opinions, list_taiwan_posts, or search_human_opinions. The agent must infer that a known post id is required and that this is the detail-fetch counterpart to the list 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?

No annotations are provided, so the description carries the full behavioral burden. It indicates the result is a curated/summarized set of human opinions rather than raw posts, but says nothing about permissions, cost, rate limits, freshness, or response shape for what is presumably a read call.

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?

Purpose is front-loaded in one short sentence, followed by the value list. The list is long by necessity and each entry is informative, though as a run-on enumeration it would be more naturally placed as a schema enum.

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 simple single-parameter read tool this covers purpose and admissible values, but with no output schema, no annotations, and an overlapping sibling, the agent still lacks return-format and result-count context. Adequate but with clear 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?

Schema coverage is 0% and the schema only types topic as a bare string, so the description does the heavy lifting by enumerating valid slugs with Chinese glosses, e.g. mechanical-keyboard(機械鍵盤), vpn(VPN). It does not state that these are the only accepted values or what happens for an unrecognized slug, keeping it below a 5.

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: fetch curated real-person opinions for popular topics. The enumerated slug list makes the domain unmistakable. It falls short of a 5 because it never distinguishes itself from the sibling search_human_opinions, which covers overlapping territory.

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 topic list implicitly scopes when the tool applies (only these popular topics), which is useful implied guidance. However, there is no explicit when-to-use vs when-not, no prerequisites, and critically no routing against the overlapping sibling search_human_opinions.

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 carries the full behavioral burden. It does disclose the provenance signals attached to results (human verification, AI-text labeling, reader voting), which is genuinely useful context, but says nothing about volume, pagination, ordering, permissions, or what a returned post looks like.

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 compact sentence with the primary content type front-loaded and filters trailing. No filler, though the parenthetical is dense and would benefit from clearer separation.

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 no annotations, no output schema, and 0% parameter description coverage, the description should explain what 'query' does and what results contain. It hints at result metadata and two filters but leaves the agent guessing on a third parameter and on result shape.

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 none of the three parameters are documented in the schema. The description loosely maps to two of them (topic filtering, human_only '只看像真人的') but gives no syntax, expected values, or interaction between query and topic, leaving the 'query' parameter entirely unexplained.

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 (列出) and resource (台灣真人使用心得), and the parenthetical content types (真人驗證、AI 文字標示、讀者投票) sharpen what the listing contains. It is distinguishable from get_taiwan_post (single post) and search_human_opinions (search), though it does not name siblings explicitly.

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 only says it can filter by topic and by human_only ('可篩主題與只看像真人的'), which restates the filters rather than saying when to choose this tool over check_ai_text, get_topic_opinions, or search_human_opinions. No when-not or alternative routing is given.

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 carries the burden and does well: it discloses that each result carries a credibility score, that the corpus spans named forums, and that before_chatgpt restricts to pre-2022-11-30 content. It omits limits on result count/pagination but covers the core behavioral traits.

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 dense sentence separated by semicolons; the core purpose is front-loaded and every clause (sources, credibility, cutoff, translation) adds distinct information without filler. Dense but earns its length.

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 2-parameter, no-output-schema, no-annotation search tool, the description covers sources, credibility scoring, the cutoff flag, and translation behavior. Only minor gaps remain, such as result volume or pagination expectations.

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 only 50% (before_chatgpt has no schema description), but the description compensates by explaining that before_chatgpt=true returns only content dated before 2022-11-30, and adds that Chinese queries are auto-translated, extending beyond the schema's example-only query description.

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 (搜尋) and resource (真人寫的意見與使用經驗), enumerates concrete sources (PTT, Hacker News, Stack Exchange, Lemmy) and explicitly marks the corpus as non-AI-generated. It clearly contrasts conceptually with check_ai_text, though it does not name any sibling directly.

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 context for using it is implied (finding human opinions/experiences), and the before_chatgpt flag hints at a use case, but there is no explicit when-to-use vs when-not guidance or routing to alternatives like get_topic_opinions or check_ai_text.

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