AI 與科技名詞白話解釋

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

With no annotations, the description must carry the full behavioral burden. It does disclose the shape of the output (suggested answer structure, structured data type, related site content), which is genuine value since no output schema exists, but it says nothing about execution characteristics, auth needs, or whether classification is deterministic.

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 with the classification task front-loaded and the returned artifacts trailing. Nothing is wasted, though the enumeration of four intents plus three outputs in one clause makes it slightly heavy.

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, no-annotation, no-output-schema tool, the description covers purpose and rough returns but omits the parameter contract and any routing guidance among the five siblings. An agent knows what it does but not confidently when to prefer it.

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 single required parameter 'q' has no schema description. The description partially compensates by defining the input as 'a search or question', but adds no guidance on language, length, or formatting of that string.

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 pairs a specific verb (classify/judge) with a concrete resource (a search query or question) and enumerates the four target categories, plus the artifacts returned. It is clearly distinct from retrieval-oriented siblings like search_glossary or get_howto, though it never names them 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?

There is no statement of when to call this tool versus the siblings (explain_term, compare_concepts, get_howto, list_topics, search_glossary) and no exclusions or prerequisites. Usage is only inferable from the fact that it takes a query.

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 full burden, and it partially meets it by disclosing the return content (結論、比較表、適用情境). It says nothing about side effects, permissions, or rate limits, though the tool is evidently a read/query operation.

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 sentences, front-loaded with concrete examples and then output/parameter behavior, with no filler. Well-sized for a simple lookup tool.

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?

No output schema or annotations exist, so the description must stand alone; it covers output shape and input modes but omits the meaning of 'id' and any failure/edge behavior, leaving modest gaps for a 3-param 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?

Schema coverage is 0% and the parameters are bare strings, so the description must compensate. It does add meaning by explaining the two input modes (id or a/b pair), but leaves 'id' undefined (an id of what?) and does not clarify whether a/b are names or slugs.

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 (compare two AI concepts) and grounds it with concrete examples (RAG vs 微調, MCP vs API). It implicitly distinguishes itself from siblings like explain_term (single-term explanation) by focusing on pairwise comparison, though it never names those alternatives.

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 examples imply when the tool is useful (comparing two concepts), and the last sentence clarifies it accepts either an id or two terms. However, there is no explicit when-to-use versus when-not guidance or routing to siblings such as explain_term for single concepts.

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 does disclose the response composition (definition, how it works, examples, misconceptions, FAQ, original sources), which is genuinely useful since no output schema exists, but it says nothing about handling unknown terms, sourcing/grounding limits, or response length.

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 sentences, front-loaded with the verb and resource before the details. Acceptably sized and free of filler; the example list is a bit long but earns its place by disambiguating the term scope.

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?

A simple one-parameter lookup with no output schema, and the description does explain what the response contains and what input forms are accepted. Only the lack of routing guidance relative to sibling tools keeps it short of 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?

Schema coverage is 100% for the single 'term' parameter, so the baseline is 3. The description adds meaning beyond the schema by stating that Chinese, English, and aliases are all accepted, clarifying the matching semantics the schema does not express.

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 (解釋 / explain) and resource (AI 或科技名詞), with concrete examples (MCP, RAG, AI 代理人, 幻覺, GEO, x402) and an enumeration of what the answer contains. The purpose is unmistakable, though it never explicitly distinguishes itself from siblings like search_glossary or compare_concepts, which could plausibly overlap.

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 when-to-use guidance and no mention of alternatives. With siblings such as search_glossary, compare_concepts, and get_howto present, the agent must guess whether this tool or a sibling is the right entry point; the language note (中文/英文/別名) is a matching detail, not usage guidance.

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 usefully discloses the shape of the returned content (需求、步驟、程式碼與常見錯誤 / requirements, steps, code, common errors), which tells the agent what a result contains. It says nothing about permissions, rate limits, or how the tutorial is selected, leaving real gaps for a no-annotation 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?

A single dense sentence that front-loads the verb and resource before listing topics and output contents. No filler, though the parenthetical topic list is long enough to slightly blunt the core purpose.

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 does describe the return payload (requirements, steps, code, common errors), which is helpful. However, a two-parameter tool with an undocumented id parameter and no usage routing leaves the agent guessing on inputs and on when to pick this over its four lookup-style siblings.

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%: q has a description ('關鍵字，例如 MCP、llms.txt') but id has none. The description never explains the parameters or how q and id relate — e.g., whether id retrieves a specific tutorial while q is a keyword search. 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?

States a specific verb+resource (取得...教學 / 'get implementation tutorials') and enumerates the covered topics (MCP servers, llms.txt, FAQPage JSON-LD, robots.txt, x402, token counting). This distinguishes it from conceptual siblings like explain_term and compare_concepts, though it never names those alternatives 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?

There is no when-to-use or when-not-to-use guidance. The agent must infer from the resource name that this is for step-by-step implementation guides rather than definitions (explain_term), topic lists (list_topics), or glossary lookups (search_glossary). No prerequisites or exclusions are stated.

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 must carry the full behavioral burden. It only says the tool lists terms and allows category filtering; it does not disclose read-only nature, return format, pagination, or what the 'comparison' and 'tutorial' outputs entail.

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 a single, efficient sentence with no wasted words. It is front-loaded with the main list action, though folding in comparisons and tutorials reduces immediate clarity.

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 list tool with several overlapping siblings, the description should clarify what is returned and how it differs from compare_concepts, get_howto, and search_glossary. It leaves that gap unfilled and does not mention return values or usage boundaries.

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%, but the description notes that results are filterable by category, and the schema supplies the enum values. This adds some meaning beyond the raw property, though it does not explain the category meanings or default behavior.

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 verb (列出) and resource (所有名詞), but then adds '概念比較與實作教學', which muddles the scope and overlaps with siblings like compare_concepts and get_howto. It does not distinguish itself from those alternatives.

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 only guidance is that results can be filtered by category. There is no indication of when to use this tool instead of search_glossary, compare_concepts, explain_term, or get_howto.

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, yet it discloses almost nothing beyond 'fuzzy search'. Ranking behavior, result count defaults, and whether results are paginated or truncated at the limit are all unstated.

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 well-formed sentence with the action and scope front-loaded; nothing redundant. It is efficient, though its brevity is partly under-specification rather than true density.

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 annotations, no output schema, and 0% schema coverage on two params, the description does far too little. A search tool needs at least query semantics, result-shape hints, and distinction from the other lookup tools.

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% on 2 parameters. The word 模糊 hints that q accepts imprecise input, but the limit parameter (1-20 cap) is completely unexplained in both schema and description, leaving the caller to guess its meaning and default.

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 (模糊搜尋/fuzzy search) and enumerates the resource scope: AI terms, concept comparisons, and practical tutorials. An agent can grasp the topic, but the description does not distinguish this tool from siblings like explain_term, compare_concepts, or get_howto, which cover the same content types.

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 on when to use this versus the sibling lookup tools (explain_term, compare_concepts, get_howto, list_topics). The word 模糊 ('fuzzy') implies an imprecise-query use case but this is never stated as a selection criterion.

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