ecpro-mcp

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns lowest, median, average, highest price and price ranges, indicating a read-only operation. However, it does not mention data freshness, regional scope beyond Taiwan, or any potential side effects.

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 extremely concise with only two sentences: the first states the output, and the second states when to use. Every sentence adds value with no wasted words.

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 simple parameter and no output schema, the description adequately covers purpose and usage. It does not explicitly describe the return format, but the listed outputs (lowest, median, etc.) imply a structured result.

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 single parameter 'category' is fully described in the input schema (100% coverage). The description provides examples but adds no additional semantics beyond the 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?

The description clearly states the tool's purpose: querying market conditions (lowest, median, average, highest price and price ranges) for a product category in Taiwan. It also provides example user queries that trigger this tool, distinguishing it from sibling tools like compare_prices or get_price_history.

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 explicitly specifies when to use the tool: when users ask about price range, market condition, or budget. It implies use cases with example queries but does not explicitly state when not to use or mention alternative 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?

No annotations provided, so description carries full burden. It discloses the three checks performed (fraud list, archive, registration). Does not disclose read-only nature or output format, but sufficient for a lookup 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?

Two concise sentences: first states action and scope, second states when to use. No wasted words, front-loaded with key information.

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 one parameter, no output schema, and no annotations, the description covers purpose, usage context, and parameter example. Could mention output format, but not critical.

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 100% with parameter description. Description adds value by specifying Taiwan-specific context, example format, and scope of checking, enriching the schema's basic 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?

The description clearly states it checks the safety of Taiwan online shopping sites by comparing against fraud reports, archives, and company registrations. It specifies the trigger question from users, distinguishing it from sibling tools that handle prices.

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 says when to use: when users ask about website safety before buying. Does not mention when not to use or alternatives, 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.

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

No annotations provided, so description carries full burden. It discloses the data sources and return types, but does not detail potential limitations, freshness, or authentication needs. Adequate but not thorough.

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?

Single sentence efficiently conveys purpose, targets, returns, and usage triggers. No extraneous information; front-loaded with key actions.

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 single parameter and no output schema, the description adequately explains the return values and context. The sibling tools are well differentiated, and the tool's purpose is fully covered.

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 parameter 'query'. The description adds value by advising precision and providing an example, going beyond the schema definition.

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 compares prices across specific marketplaces (momo, PChome, Taiwan brand official websites) and returns multiple outputs (overall lowest, per-channel lowest, candidate list). It distinguishes itself from siblings like get_price_history and search_products.

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 mentions when to use (user asks 'where to buy cheapest', 'help compare prices'). Lacks explicit when-not-to-use, but given sibling context, it provides adequate 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?

The description mentions 'real-time exchange rate', indicating a live query. With no annotations provided, it carries full burden but omits details like potential rate limits, approximation, failure behavior, or output format, leaving moderate 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 sentences long, front-loaded with the core action, and contains zero extraneous words. Every sentence serves a purpose: stating the function and specifying when to use it.

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 one-parameter tool with no output schema, the description covers the main function and usage cues. It could be slightly enhanced by mentioning the output format (e.g., 'returns converted amounts in the requested currency'), but this is a minor gap.

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 the parameter description '台幣金額' is clear. The description does not add additional semantics beyond stating the conversion to multiple currencies, which is not parameter-specific. Baseline 3 is 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 clearly states the tool converts TWD amounts to USD, JPY, CNY, and other major currencies using real-time exchange rates. It uses a specific verb ('convert') and resource ('台幣金額'), and is distinct from all sibling tools which focus on e-commerce, pricing, or tech detection.

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 usage context: 'when users ask 'how much is this in USD/JPY' or 'cross-border price comparison''. However, it does not mention when not to use the tool or name alternative tools, missing an opportunity to prevent misuse.

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 describes the output as a ranking list of products with largest price gaps but does not disclose data freshness, update frequency, or category limitations. Adequate but not thorough.

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 Chinese sentences, front-loaded with purpose and immediately followed by usage examples. No wasted words.

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 tool with one parameter and no output schema, the description sufficiently explains what it does and when to use it. The return format is implied but not detailed, which is acceptable given the tool's straightforwardness.

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 only parameter (limit) is fully described in the schema (回傳筆數，預設 15，最多 30). The description adds nothing beyond the schema. With 100% schema coverage, baseline is 3.

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 purpose: finding products with the largest price differences across momo, PChome, and brand official sites. It uses a specific verb+resource (找出排行) and distinguishes from siblings like compare_prices by focusing on cross-channel gap ranking.

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 explicitly specifies when to use: when users ask about big price differences across channels or where to buy cheapest. It does not provide negative cases or alternatives, 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.

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 lists what technologies are detected, but does not disclose behavior like rate limits, accuracy, or error handling. It adds context beyond annotations but lacks depth.

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 purpose, and no unnecessary words. 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?

The description explains purpose and input adequately but lacks details about output (no output schema). For a detection tool, return value description would be helpful, but it's not required.

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 (url) with schema coverage 100%. The description adds no extra meaning beyond the schema's description of '網址或網域', so baseline 3 applies.

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 it detects e-commerce technologies (開店平台, 金流, 廣告像素, 分析追蹤, 行銷工具) from a given URL. It lists specific examples (SHOPLINE, 91APP, Shopify, WooCommerce) and distinguishes from sibling tools which are about prices and articles.

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 explains that input is a URL or domain, and implicitly when to use it (to detect tech stack). However, it does not explicitly state when not to use it or provide alternatives among siblings (though siblings are unrelated).

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 full burden. It accurately discloses the tool's behavior: finding historical lows, deepest discounts, and category min prices. It does not mention side effects or data freshness, but for a read-only deal finder this is acceptable and fairly transparent.

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 extremely concise: two sentences in Chinese that are front-loaded with the core purpose and followed by usage guidance. No unnecessary words, 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?

The tool has 3 optional parameters and no output schema. The description explains what it returns (deals of various types) and covers usage context. While it could mention output format or limitations, it is fairly complete for an agent to invoke correctly.

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%, so each parameter is described. The description adds overall context but does not significantly enhance individual parameter meaning beyond the schema. Baseline of 3 is appropriate; the description provides purpose but not deeper semantics for each param.

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 finds current deals on Taiwan e-commerce, specifying types like all-time lows, deepest discounts, and category minimums. It also gives usage examples, effectively distinguishing from sibling tools like compare_prices or get_price_history.

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 explicitly tells when to use the tool (e.g., when users ask about deals or price drops) and provides concrete query examples. While it lacks explicit 'when not to use' statements, the context is clear enough for an agent to select it appropriately among 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 must carry the full burden of behavioral disclosure. It mentions the tool performs a reverse lookup but does not describe return format, pagination, rate limits, data freshness, or whether the list is exhaustive. For a search tool, this lack of detail reduces transparency significantly.

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 consists of two concise sentences: the first defines the tool's purpose, the second provides usage context with examples. Every sentence is essential; there is no redundancy or excess. It is front-loaded with the main action.

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's low complexity (2 parameters, no output schema), the description is partially complete: it explains what the tool does and when to use it. However, it omits details about return values and behavior, which are necessary since no output schema exists. More information about the result format would improve 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?

The input schema covers 100% of parameters (tech, limit), with clear descriptions for each. The tool description adds no additional semantic meaning beyond the schema: it merely repeats the tech parameter's purpose. Per guidelines, with high schema description coverage, the baseline is 3.

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 '反查台灣有哪些網站使用某個電商技術' (reverse lookup which websites in Taiwan use a certain e-commerce technology), specifying the verb (search/find), resource (sites), and scope (Taiwan, e-commerce tech). It distinguishes from sibling tools like 'detect_site_tech' which detects tech on a given site, making the unique purpose evident.

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 when-to-use guidance: '使用者問「用 SHOPLINE 的台灣網站有哪些」「哪些站在用綠界金流」時使用' (use when users ask for sites using a specific tech). It gives concrete examples. While it does not explicitly state when not to use or name alternatives, the context is clear and sufficient for common use cases.

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, so description carries full burden. States returns plain text content plus FAQ. Implies read-only operation. Could mention no side effects, but sufficiently transparent for a simple read 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?

Two sentences, front-loaded action, no unnecessary words. Every sentence serves a clear purpose: stating function and usage order.

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?

All essential information covered: what it does, how to use it, prerequisite step. No output schema, but description notes output is plain text with FAQ. Minor gap: no mention of error handling or pagination, but appropriate for a simple 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?

Only one parameter (slug). Schema already describes its type and example. Description adds context that slug comes from search_ecommerce_articles, adding value 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?

Clear verb+resource: '取得 ECPRO 電商博士單篇文章的完整內容' (get full content of a single article). Distinguishes from siblings by referencing search_ecommerce_articles as prerequisite.

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?

Explicit guidance: '先用 search_ecommerce_articles 找到 slug 再呼叫' (first use search_ecommerce_articles to find slug, then call this). Tells when to use this tool vs. alternatives.

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 must cover behavioral traits. It does not mention time range, data recency, authentication, rate limits, or any side effects. The description only states the output nature but lacks important behavioral details.

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, no redundancies, directly conveys purpose and usage. 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 a single parameter, no output schema, and no annotations, the description provides adequate context: what it does, how to get the input, and why it is useful. Lacks details about output format or time range, but is sufficient for basic understanding.

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% with a basic description of offer_id (example). The description adds meaningful context by stating the offer_id comes from other tools, which helps in understanding how to obtain the required 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?

Clearly states it retrieves historical price trend (date + price) for a single product and explains the use case (judging if current price is low or a real discount). Distinguishes from siblings like search_products and compare_prices.

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 tells where to get the offer_id (from search_products or compare_prices), implying the tool should be used after those. Does not explicitly mention when not to use it, but context is clear.

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?

The description states it lists categories and counts, which is a simple read-only operation. There are no annotations, so the description carries the burden. It clearly describes the output behavior without revealing any side effects. However, it could mention that it returns all categories at once.

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, front-loaded sentence that directly states the action and purpose. Every word is informative with no redundancy.

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 no parameters, no output schema, and a simple task, the description is fully complete. It explains what it does and why to use 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?

There are no parameters, so the baseline is 4. The description does not need to add parameter details. The schema coverage is 100% (empty 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?

The description clearly states that the tool lists all article categories and their article counts, and its purpose is to understand the coverage of the knowledge base. This distinguishes it from sibling tools like 'search_ecommerce_articles' which search articles, or 'compare_prices' which handles pricing.

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 explicitly mentions the use case: '用来了解知识库涵盖范围' (used to understand the coverage of the knowledge base). It implies when to use it, but does not provide explicit when-not-to-use or alternatives.

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 were provided, so the description carries the full burden. It discloses that the tool queries the Open Food Facts database and returns ingredients, nutrition, allergens. However, it lacks mention of potential limitations (e.g., database coverage, online requirement).

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 but somewhat repetitive. The first sentence states the function, the second states the usage context. It could be more concise but is acceptable.

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 simplicity of the tool (1 parameter, no output schema), the description is adequate. It covers purpose and usage context. No critical information is missing.

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 for the single parameter (barcode). The description does not add extra meaning beyond what the schema already provides ('商品條碼（EAN/UPC 數字）'). Baseline 3 is 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 clearly states the verb (查/lookup) and resource (食品的成分、營養、過敏原 via Open Food Facts). It is specific and distinguishes from sibling tools which are pricing/search 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?

The description provides a clear use case: '使用者掃到條碼、問食品成分/營養時使用' (when user scans barcode or asks about ingredients/nutrition). It does not explicitly state when not to use, 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.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the tool returns cheaper channels and savings, but lacks details on operational behavior such as data freshness, scope of channels searched, or any side effects. This is insufficient for a tool with no annotations.

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 highly concise, consisting of two sentences that front-load the main purpose and include example triggers. Every sentence adds value, with no wasted words.

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 tool with 2 parameters and no output schema, the description covers inputs, output, and use cases adequately. However, it lacks details on the output format (e.g., whether it returns a list or single result) and any constraints, preventing a perfect score.

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%, with both parameters already described. The description reinforces their meaning (product name, paid price) but adds no new semantic details beyond the schema. Baseline score of 3 is 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?

Description clearly states tool checks for overpayment by comparing prices across channels, with specific inputs (product name, paid price) and outputs (cheaper alternatives, savings). It includes example queries, making the purpose highly specific and distinct from siblings like compare_prices or find_deals.

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?

Description explicitly advises use when users ask 'Did I overpay?' or 'Is this price reasonable?' This provides clear context, though it does not mention when not to use it or list alternative tools. The guidance is adequate but not exhaustive.

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 full responsibility. It discloses that the tool recommends products based on budget and scenario, but it does not detail output format, side effects, or required permissions. This is adequate for a straightforward recommendation tool but lacks extra context.

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: one defining purpose and one providing usage examples. Every word is functional with no waste. It is front-loaded with the core action.

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 output schema and no annotations, the description covers purpose, usage scenarios, and parameter roles. It would benefit from mentioning what the output looks like (e.g., a list of products with prices), but it is largely sufficient for an agent.

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%, with all parameters already described in the schema. The description adds usage examples and clarifies the 'scene' parameter's role (e.g., 送禮, 學生), but does not significantly enhance meaning beyond the schema. Baseline 3 is 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 clearly states the tool recommends Taiwanese products by budget and scenario (gifting, students, renters, etc.). It provides example queries that make the purpose unmistakable. This distinguishes it from sibling tools like search_products or find_deals.

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 specific example queries that trigger this tool (e.g., 'X元送禮推薦什麼'), providing clear context for when to use it. However, it does not explicitly state when not to use it or list alternative 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 burden. It discloses return fields (title, summary, category, URL) and the general behavior of searching. However, it lacks details on result ordering, pagination, or search logic, which are typical for search tools.

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, front-loading the purpose and scope, then covering return fields and usage context. Every sentence is meaningful with no 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?

Given no annotations or output schema, the description adequately explains what the tool does, its parameters, and return format. It lacks details on sorting or pagination, but is sufficient for a straightforward search 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 100%, so baseline is 3. The description adds examples for query and default/max for limit, but does not provide significant additional meaning beyond the schema descriptions.

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 searches for e-commerce knowledge articles from ECPRO, specifies the language and topics covered, and distinguishes from siblings like search_products and get_ecommerce_article by focusing on articles rather than products or single article 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?

The description explicitly recommends use when answering questions about Taiwan e-commerce operations, providing a clear usage context. However, it does not mention alternative tools or when not to use this 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 adequately discloses the tool's behavior: it is a search tool (read-only), returns specific fields (name, price, channel, link), and sorts by price ascending. It does not mention auth requirements or side effects, but for a search tool, the behavior is reasonably transparent.

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 very concise: two sentences covering scope, return values, sorting, and use cases. It is front-loaded with the key information and has no filler. Every sentence serves a 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?

Given the tool has 5 parameters (1 required) and no output schema, the description sufficiently covers what the tool does, what it returns, and when to use it. It could be enhanced by mentioning pagination (implied by limit) or error handling, but overall it is complete for typical usage.

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?

All 5 parameters are described in the schema (100% coverage). The description adds some context, such as mentioning the target channels (momo, PChome, official) which aligns with the channel parameter, but does not add significant meaning beyond the schema descriptions. The baseline of 3 is 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 clearly states that the tool searches for products and prices across specific Taiwanese e-commerce sites (momo, PChome, official brand stores), returns product name, current price, channel, and purchase link, and is sorted low to high. It also distinguishes itself from siblings like compare_prices and get_price_history by focusing on product 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 explicitly mentions when to use the tool (when user wants to buy something, ask price, or need recommendations), providing clear context. However, it does not specify when not to use it or mention alternatives, which would be helpful for differentiation from similar 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 bears full burden. It discloses that the tool uses historical prices and recent trends, implying a read-only analysis. However, it does not detail potential limitations, output format, or any side effects.

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 (two sentences) and well-structured, starting with purpose, then usage scenarios, then parameter provenance. No redundant information.

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?

Despite having no output schema or annotations, the description covers purpose, usage guidance, and parameter source. It could be slightly improved by hinting at the output format (e.g., a buy/wait recommendation), but it is adequate for 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?

The parameter schema already has 100% coverage with a description and example. The tool description adds valuable context by specifying that offer_id is obtained from sibling tools (search_products / compare_prices), which helps the agent.

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 purpose: determining if a product is a good buy now by analyzing historical prices and trends. It explicitly mentions use cases like 'Should I buy X now?' and distinguishes from siblings like get_price_history and find_deals.

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 explicit when-to-use scenarios (user questions about buying now or price fairness) and tells the agent how to obtain the required offer_id from sibling tools. It lacks explicit when-not-to-use statements but provides sufficient guidance.

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