台灣 AI 工具價格

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 scope of comparison (self-hosting, pricing, MCP support, Traditional Chinese UI) and an unexpected extra output (Taiwan-available autonomous AI assistants), but says nothing about read-only nature, latency, or return structure.

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 front-loaded sentence with the action verb leading and the platform list and dimensions packed efficiently. Dense but no wasted 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?

A zero-parameter, read-only query tool with no output schema. The description covers what is compared and what is returned, which is adequate for its simplicity; deeper return-format detail is not strictly required here.

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 tool takes zero parameters, so per the rubric the baseline is 4. There is no parameter information to add meaningfully.

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 (比較) and resource (AI 代理工作流平台), enumerating the exact platforms compared (n8n, Make, Zapier, Dify, Coze, Copilot Studio, Agent SDK) plus the comparison axes. It also notes a secondary return (台灣可用的自主 AI 助理). Clear enough to distinguish from siblings like compare_ai_subscriptions, 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?

The description lists comparison dimensions but gives no explicit when-to-use or when-not-to-use guidance, and does not point to alternatives such as compare_ai_subscriptions or get_ai_api_pricing. The agent must infer when this tool is preferable to the siblings.

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 disclosure burden. It usefully states that results include official sources, giving the agent provenance confidence, but says nothing about data freshness, pricing period (monthly vs annual), or whether figures are converted or quoted. For a read-only comparison tool the risk is low, but the behavioral picture is only partially filled in.

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 front-loaded sentence beginning with the action verb, with no filler. The vendor enumeration is slightly long, but it directly communicates coverage scope, so it mostly earns its space.

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 low-complexity tool with one optional parameter, no annotations, and no output schema, the description conveys enough to invoke it correctly: what is compared, in what currency, for what region, and with cited sources. Only the absence of freshness/update expectations and of a sense of the returned structure keeps it short of full completeness.

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 a single optional parameter and schema description coverage is 100%, so the schema already documents the 'product' keyword and the default-to-all behavior. The description adds no parameter-level meaning beyond what the schema provides, making the baseline of 3 appropriate.

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 (compare) plus the exact resource (consumer AI subscription plans) and enumerates the vendors covered (ChatGPT, Claude, Gemini/NotebookLM, Perplexity, M365 Copilot). It also pins the scope to Taiwan and TWD pricing and features, which cleanly separates it from siblings like get_ai_api_pricing and estimate_ai_api_cost_twd (API-level costs) and compare_agent_platforms (agent platforms).

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 scope statement ('subscription plans', 'in Taiwan') implies when this tool is the right pick, but there is no explicit when-to-use, when-not-to-use, or named alternative among the four siblings. An agent must infer that this is for consumer subscription comparison rather than API cost tools.

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 that results are returned in both USD and TWD, which is genuine output behavior, but it says nothing about whether live pricing is used, pricing staleness, required auth, or rounding/currency-conversion assumptions.

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 tight sentences: purpose and currency first, then parameter routing. No filler, and the most decision-relevant information (currency of output, which param for which modality) is 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 7-parameter estimation tool with no annotations and no output schema, the description covers the main input-routing cases but omits the meaning of cached_tokens and the source/freshness of the pricing data, leaving the agent unable to fully call it correctly for cached-token scenarios.

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% with 7 parameters, so the description must compensate, and it does: it explains which inputs correspond to which model type, effectively documenting five of the seven parameters. cached_tokens and the semantics of model are left unexplained, so it is not a full 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?

The description leads with a specific verb and resource, '估算…花費' (estimate … cost), and names the two output currencies (USD and TWD), so the agent knows exactly what is produced. It does not explicitly distinguish itself from the sibling get_ai_api_pricing, which is a pricing lookup rather than a calculator, so it falls short of a 5.

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 gives useful input-routing guidance by mapping model modality to the parameter to supply (text→input_tokens/output_tokens, image→images, video→seconds, speech→characters). However, it never states when to use this tool versus siblings like get_ai_api_pricing or get_ai_api_pricing-plus-subscription tools, nor any exclusions or prerequisites.

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 adds useful context beyond a bare query by disclosing the unit basis of the prices and the TWD currency conversion, which is non-obvious. However, it omits data freshness/source reliability, whether rates are cached, and permission requirements — meaningful gaps for an unannotated 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 sentence with no padding; the core action is front-loaded and the parenthetical detail is compact. Efficient and readable, though the enumeration makes it slightly dense.

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 zero required parameters, no output schema, and no annotations, the description should explain the no-filter default behavior and the shape of returned pricing data. It covers the content domain reasonably but leaves the vendor filter and default-result behavior unexplained, so it is only minimally 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 description coverage is 0% across both parameters. The description enumerates the four pricing categories (text, image, video, audio), which partially maps to the category enum values, but it says nothing about the vendor parameter or that both filters are optional. It only partially compensates for the documentation 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 states a specific verb (查詢/query) and resource (AI API 官方價格/official AI API pricing), and enumerates the units covered (per million tokens, per image, per second, per audio). This clearly differentiates it from pricing-adjacent siblings like estimate_ai_api_cost_twd (a computed estimate) and compare_ai_subscriptions. It stops short of explicitly naming which sibling it is not, so it lands at 4 rather than 5.

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 explicit when-to-use guidance and no mention of alternatives such as estimate_ai_api_cost_twd, which an agent could easily confuse with a price lookup. The purpose implies a lookup scenario, but the agent must infer when this is preferable to the cost-estimation or subscription-comparison siblings.

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, and it discloses almost nothing: no indication of whether the content is static or updated, sourced, region-restricted, or what output language/format to expect. The one faint signal is that the content is Taiwan-specific and includes politically sensitive material (government bans), which hints at scope but not behavior. For a retrieval tool this is a real gap, though the read-only nature means low risk.

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-fragment with no padding, and the domain qualifier ('台灣專題') is front-loaded ahead of the topic breakdown. It is efficient but under-structured as prose, since it lacks a verb and reads as a label list rather than a sentence. Nothing wasted, nothing redundant.

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-output-schema, no-annotation tool the definition is minimally workable, but because there is no output schema the description should say something about what is returned (format, language, freshness) and it does not. The content coverage per topic partially compensates by signalling scope. Adequate but with a clear gap around the return value.

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 'topic' parameter is an enum, but the description compensates well by mapping each enum value to distinct content (notebooklm = team collaboration and quotas, gemini = Taiwan pricing and student discounts, deepseek = government bans and enterprise compliance). This meaningfully helps an agent choose the right topic value beyond the bare enum labels. The only shortfall is that it does not clarify what difference the topic makes to the response.

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 enumerates the three Taiwan-specific topics (notebooklm, gemini, deepseek) and what each covers, so the resource is identifiable and implicitly distinct from the pricing/comparison siblings. It never states an explicit verb (retrieve/return) and reads as a noun fragment, so it falls short of a fully self-contained purpose statement. Combined with the tool name, an agent can tell what this retrieves, but it is not sharp on its own.

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, no prerequisites, and no mention of alternatives such as get_ai_api_pricing or compare_ai_subscriptions, even though those siblings overlap on the same vendors. The only implicit steering comes from the topical scope of the enum values. An agent gets no signal about when this advisory tool is preferred over the comparison tools.

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