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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool returns URLs rather than actual search results, and those URLs need to be accessed via WebFetch tool. This provides important context about the tool's output format and required follow-up actions, though it doesn't mention rate limits, authentication needs, or error handling.

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 efficient with only two sentences. The first sentence states the purpose, and the second provides critical usage guidance. Every sentence earns its place, and the information is front-loaded with the most important guidance highlighted with 【重要】.

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 moderate complexity (search with URL return), no annotations, and no output schema, the description does well by explaining the key behavioral constraint (returns URLs requiring WebFetch). However, it doesn't explain what the returned URL structure looks like or provide examples, which would help the agent understand the output better.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so both parameters are well-documented in the schema. The description doesn't add any additional parameter semantics beyond what's in the schema (query for keywords, product for product names). The baseline of 3 is appropriate when the schema does the heavy lifting.

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 Alibaba Cloud product documentation and APIs, with a specific verb ('搜索') and resource ('阿里云产品文档和API'). It distinguishes from generic search tools but doesn't explicitly differentiate from sibling tools like 'ai_search_tencent_docs' or 'ai_search_docs' beyond mentioning Alibaba Cloud.

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 clear context about when to use this tool (for Alibaba Cloud documentation) and includes an important usage note about needing to use WebFetch tool to access the returned URL. However, it doesn't explicitly state when NOT to use it or provide alternatives among the many sibling search 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 provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool returns a search URL rather than actual search results, which is important behavioral information. It also implies this is a read-only operation (searching documentation) and provides guidance on how to use the output. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions.

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 and well-structured. The emoji and title immediately convey the tool's domain. The important behavioral information is highlighted with 【重要】and presented clearly. Every sentence earns its place by providing essential guidance that isn't available elsewhere.

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 search tool with no annotations and no output schema, the description does an excellent job of explaining what the tool does and how to use its output. The critical information about returning URLs rather than content is clearly stated. The main gap is the lack of output format details - while we know it returns URLs, we don't know the exact structure of the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with both parameters clearly documented in the schema. The description doesn't add any additional parameter semantics beyond what's already in the schema. It doesn't explain how the two parameters interact or provide examples of valid values. The baseline score of 3 is appropriate when the schema already provides complete parameter documentation.

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 as searching for API documentation and usage examples with the phrase '快速查找API文档和使用示例' (quickly find API documentation and usage examples). It specifies the verb '搜索' (search) and resource 'API文档' (API documentation). However, it doesn't explicitly differentiate from sibling tools like ai_search_docs or ai_search_web, which likely have overlapping functionality.

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 excellent usage guidance with the explicit instruction 'Claude Code应该使用WebFetch工具访问该URL以获取真实搜索结果' (Claude Code should use the WebFetch tool to access this URL to get real search results). This tells the agent exactly how to use the output and what the next step should be, which is crucial for proper tool chaining.

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 full burden. It discloses a crucial behavioral trait: '此工具会返回博客园搜索URL，Claude Code应该使用WebFetch工具访问该URL以获取真实搜索结果' (This tool returns Cnblogs search URLs, Claude Code should use WebFetch tool to access the URL for actual search results). This reveals the tool doesn't return actual content but rather URLs that require secondary fetching, which is important operational context not inferable from the schema alone.

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 appropriately concise with two sentences that both earn their place. The first sentence states the core purpose, the second provides crucial operational guidance. The formatting with emoji and brackets adds visual structure without being excessive. It could be slightly more front-loaded by moving the operational guidance to a separate section.

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 moderate complexity (search function with URL return behavior), no annotations, no output schema, and 100% schema coverage, the description is partially complete. It covers the purpose and crucial behavioral trait about URL returns, but lacks information about response format, error conditions, rate limits, or authentication requirements. The absence of output schema means the description should ideally explain what the return value contains.

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 100% with the single parameter 'query' well-described as '搜索关键词' (search keywords). The description adds no additional parameter semantics beyond what the schema provides. With high schema coverage and only one parameter, the baseline of 3 is appropriate - the schema does the heavy lifting.

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: '搜索博客园技术博客' (search Cnblogs technical blogs). It specifies the verb (search) and resource (Cnblogs technical blogs), but doesn't explicitly differentiate from sibling tools like ai_search_csdn or ai_search_juejin which also search Chinese technical content platforms. The emoji and formatting add clarity but don't provide sibling differentiation.

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 no guidance on when to use this tool versus alternatives. With 13 sibling tools all performing similar search functions across different platforms (CSDN, GitHub, StackOverflow, etc.), there's no indication of when Cnblogs is the appropriate choice versus other Chinese technical platforms like CSDN or SegmentFault, or versus general web search.

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 full burden and does well by disclosing key behavioral traits: it returns CSDN search URLs rather than actual content, and requires a follow-up action with WebFetch tool. This transparency about the tool's limited scope and required workflow is valuable context not available elsewhere.

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 perfectly front-loaded with the core purpose, followed by crucial workflow information. Every sentence earns its place: the first establishes what it does, the second provides critical implementation guidance. No wasted words or unnecessary elaboration.

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 search tool with no annotations and no output schema, the description provides good completeness by explaining the tool's limited scope (returns URLs only) and required follow-up action. However, it doesn't mention potential limitations like rate limits, authentication needs, or result format expectations, which would be helpful given the lack of structured output information.

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 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3. No compensation is needed given the complete schema coverage.

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 specific action ('搜索CSDN技术博客和问答') and resource ('CSDN搜索'), distinguishing it from sibling tools that search other platforms like GitHub, StackOverflow, or Chinese tech blogs. The emoji and Chinese text add clarity about the Chinese-language focus.

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 clear context about when to use this tool (for searching CSDN technical blogs and Q&A) and explicitly mentions the alternative tool ('WebFetch') needed to access actual results. However, it doesn't specify when NOT to use it compared to other sibling search tools targeting different platforms.

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. It discloses that the tool returns documentation search URLs (not the actual content), which is a key behavioral trait. However, it doesn't mention rate limits, authentication needs, error handling, or what happens with invalid inputs, leaving gaps for a mutation-like search 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?

The description is appropriately sized with two sentences: one stating the purpose and one with a critical usage note. It's front-loaded with the main function, though the emoji and formatting (brackets) add slight noise without essential 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 no annotations and no output schema, the description is incomplete for a search tool. It explains the return type (URLs) and post-processing step (use WebFetch), but lacks details on output format, error cases, or how results are filtered/scoped. The framework enum helps, but more context on behavior is needed.

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 100%, so the schema already documents both parameters (query and framework with enum values). The description adds minimal value beyond this by implying the framework parameter targets 'official documentation' for those tools, but doesn't explain semantics like what 'general' means or how queries are processed.

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 as searching official documentation for common frameworks and tools (React, Vue, Node.js, etc.), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'ai_search_api_reference' or 'ai_search_web', which might have overlapping domains.

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 clear context for when to use this tool (searching official docs for specified frameworks) and includes an important note about using WebFetch to access the returned URLs. It doesn't explicitly state when not to use it or name alternatives among siblings, but the framework focus offers implicit 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?

With no annotations provided, the description carries full burden. It discloses that the tool returns GitHub search URLs rather than direct results, which is valuable behavioral information not evident from the schema. However, it doesn't mention rate limits, authentication requirements, or error handling for GitHub API 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?

The description is appropriately sized with two sentences: one stating the purpose and scope, and another providing critical behavioral guidance about URL returns. The emoji and formatting are slightly decorative but don't detract from clarity. 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 no annotations and no output schema, the description partially compensates by explaining the URL return behavior. However, for a search tool with 4 parameters and potential complexity (e.g., GitHub API limitations), it lacks details on result format, pagination, or error scenarios, leaving gaps in 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?

Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced coverage.

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 GitHub repositories, code, issues, and users, providing a specific verb ('搜索' meaning 'search') and resource ('GitHub'). However, it doesn't explicitly differentiate from sibling tools like ai_search_web or ai_search_stackoverflow, which also perform searches but on different 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 description implies usage by specifying GitHub as the search target, but doesn't provide explicit guidance on when to use this tool versus alternatives like ai_search_web for general web searches or other platform-specific search tools. The mention of using WebFetch for results is procedural rather than contextual 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does an excellent job describing the key behavioral trait: that this tool returns a URL rather than direct search results, and that Claude Code should use WebFetch to access the actual results. This is crucial information not apparent from the input schema alone. It doesn't mention rate limits, authentication needs, or error handling, but provides the most critical behavioral information.

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 perfectly concise and well-structured. The first sentence states the core purpose. The second sentence provides the critical behavioral information about URL returns and WebFetch usage. Every sentence earns its place with essential information. The emoji adds visual distinction without compromising 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?

Given the tool's moderate complexity (search with parameters), no annotations, and no output schema, the description does quite well. It explains the purpose, provides crucial behavioral context about URL returns, and gives usage guidance. It doesn't explain return values, but with no output schema, some mention of expected results would be helpful. The description is mostly complete but could benefit from mentioning what kind of information the URL contains.

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 100%, so the schema already fully documents both parameters (query and sort with enum values). The description adds no additional parameter information beyond what's in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter info in the description, which applies here.

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 articles on the Juejin technical community with the phrase '搜索掘金技术社区文章'. It uses a specific verb ('搜索' - search) and identifies the resource ('掘金技术社区文章' - Juejin technical community articles). However, it doesn't explicitly differentiate from sibling tools like ai_search_csdn or ai_search_segmentfault that also search Chinese technical communities, missing full sibling distinction.

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 clear context for when to use this tool: searching Juejin articles. The second sentence offers important guidance about the output format (returns a URL) and how to proceed (use WebFetch tool). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling search tools, which would be needed for a perfect score.

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 full burden and does well by disclosing key behavioral traits: it returns NPM search URLs rather than direct results, and requires a secondary WebFetch call to get actual data. This is valuable information about the tool's output format and workflow requirements.

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 efficient - two sentences with zero waste. The first sentence states purpose, the second provides critical behavioral guidance. Both sentences earn their place by adding essential 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?

For a search tool with no annotations and no output schema, the description provides good completeness by explaining the tool's workflow (returns URLs requiring WebFetch). It covers the essential behavioral context needed to use the tool effectively, though it could mention typical use cases or limitations.

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 100%, providing good documentation for both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation when schema does the heavy lifting.

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 NPM packages and related documentation with a specific verb ('搜索' - search) and resource ('NPM包' - NPM packages). It distinguishes from siblings by specifying NPM as the target, but doesn't explicitly contrast with other search tools beyond the domain difference.

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 clear context about when to use this tool (for NPM package searches) and explicitly states that Claude Code should use WebFetch tool to access the returned URLs. However, it doesn't specify when NOT to use it or mention alternatives among the sibling search 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 provided, the description carries the full burden of behavioral disclosure. It helpfully reveals that the tool returns URLs rather than actual content and requires WebFetch for real results, which is valuable behavioral context. However, it doesn't mention rate limits, authentication needs, or what happens with invalid queries.

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 appropriately sized with two sentences that each serve distinct purposes: the first states the core function, the second provides critical implementation guidance. The emoji adds visual distinction but doesn't detract from clarity. The information is front-loaded with the most important details first.

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 search tool with no annotations and no output schema, the description provides adequate but incomplete context. It explains what the tool does and the critical implementation detail about URL returns, but doesn't describe the format of returned URLs, error conditions, or result limitations. Given the complexity of search tools and lack of structured metadata, more behavioral detail would be helpful.

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% description coverage with clear parameter documentation, so the baseline is 3. The tool description doesn't add any parameter-specific information beyond what's already in the schema - it doesn't explain search syntax, result limitations, or provide examples of effective queries.

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 '开源中国技术资讯和项目' (Open Source China technical information and projects), providing a specific verb (search) and resource (Open Source China content). It distinguishes itself from siblings by specifying the target platform, though it doesn't explicitly contrast with other search tools like ai_search_github or ai_search_csdn.

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 clear context for when to use this tool (searching Open Source China content) and includes an important usage note about requiring WebFetch to access actual results. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling search 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 provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond basic functionality by specifying that the tool returns a search URL (not the actual results) and requires a follow-up with WebFetch, which is crucial for understanding its limited output. It doesn't mention rate limits, authentication needs, or error handling, but the disclosed behavior is helpful and non-misleading.

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 appropriately sized and front-loaded, starting with an emoji and the core purpose. The second sentence adds critical behavioral context without redundancy. It could be slightly more structured (e.g., bullet points), but it avoids waste and earns its place efficiently.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It explains the key behavioral trait (returns a URL for WebFetch) but lacks details on error cases, result format expectations, or how it differs from sibling tools. Without an output schema, more guidance on the URL structure or next steps 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?

Schema description coverage is 100%, with both parameters ('query' and 'tags') clearly documented in the schema. The description adds no additional meaning beyond the schema, such as examples or format details for the tags parameter. The baseline score of 3 is appropriate since the schema adequately covers parameter semantics without extra value from the 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 the tool's purpose as searching SegmentFault for technical Q&A and articles, using specific verbs ('搜索') and identifying the target resource ('思否技术问答和文章'). However, it doesn't explicitly differentiate from sibling tools like 'ai_search_stackoverflow' or 'ai_search_csdn' that perform similar search functions on different platforms, which prevents a perfect score.

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 implied usage guidance by mentioning that the tool returns a URL that should be accessed with WebFetch for actual results, suggesting it's a preliminary step rather than a direct search. However, it lacks explicit when-to-use vs. alternatives among the many sibling search tools (e.g., when to choose this over 'ai_search_stackoverflow' for Chinese content), and no exclusions or prerequisites 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?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it returns URLs rather than actual content, requires a secondary tool (WebFetch) for full functionality, and implies this is a search/read operation. It doesn't mention rate limits, authentication needs, or error handling, but provides sufficient context for basic usage.

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 and front-loaded: two sentences that each earn their place. The first establishes purpose, the second provides critical usage guidance. No wasted words, with emoji and formatting that enhance readability without adding 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 the tool's moderate complexity (search with 3 parameters), no annotations, and no output schema, the description does well by explaining the critical behavioral aspect (returns URLs requiring WebFetch). It could be more complete by mentioning what the URL output format looks like or error scenarios, but covers the essential workflow adequately.

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?

With 100% schema description coverage, the baseline is 3. The description adds no additional parameter information beyond what's already in the schema (query, tags, sort parameters are fully documented in schema). The description focuses on the tool's behavioral output rather than parameter semantics.

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 with specific verb ('搜索' meaning 'search') and resource ('StackOverflow'), explicitly distinguishing it from sibling tools by naming the specific platform. It provides both Chinese and English context ('StackOverflow搜索 - 搜索技术问题和解决方案') which enhances clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives: it specifies that this tool returns StackOverflow search URLs and that Claude Code should use WebFetch to access the actual results. This creates a clear workflow distinction from other search tools in the sibling list.

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. It discloses the key behavioral trait that this tool returns URLs rather than actual search results, which is crucial information. However, it doesn't mention rate limits, authentication requirements, error conditions, or what happens when no results are found. The description adds important context but leaves gaps in behavioral understanding.

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 perfectly structured and concise. The first sentence establishes the core purpose, and the second sentence (marked with 【重要】) provides critical usage guidance. Every sentence earns its place, with no wasted words. The information is front-loaded with the most important guidance clearly highlighted.

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 moderate complexity (search with 2 parameters), no annotations, and no output schema, the description does well by explaining the key behavioral constraint (returns URLs requiring WebFetch). However, it doesn't describe the format of returned URLs, error conditions, or what happens with the optional 'product' parameter. For a search tool with no output schema, more detail about the return value would be helpful.

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 100%, so the schema already documents both parameters fully. The description doesn't add any parameter-specific information beyond what's in the schema. It doesn't explain parameter interactions, provide examples, or clarify the relationship between 'query' and 'product' parameters. Baseline 3 is appropriate when the schema does all the parameter documentation work.

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: '搜索腾讯云产品文档和API' (search Tencent Cloud product documentation and APIs). It specifies the resource (Tencent Cloud documentation) and verb (search), though it doesn't explicitly differentiate from all sibling tools beyond the cloud provider specificity. The cloud emoji and provider name help distinguish it from general documentation 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 excellent usage guidance with the 【重要】section, explicitly stating when to use this tool ('会返回腾讯云文档搜索URL' - returns Tencent Cloud documentation search URLs) and what to do with the output ('Claude Code应该使用WebFetch工具访问该URL' - Claude Code should use WebFetch tool to access the URL). This clearly establishes the tool's role in a multi-step workflow.

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 reveals the tool returns search URLs rather than actual content, which is valuable behavioral information. However, it doesn't disclose other important traits like rate limits, authentication needs, pagination behavior, or what happens with invalid queries. The description adds some context but leaves significant behavioral aspects unspecified.

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 appropriately sized with two sentences that both earn their place. The first sentence establishes the core functionality, and the second provides critical usage guidance about URL returns and WebFetch integration. The emoji and formatting are slightly decorative but don't detract from the essential information. It could be slightly more structured but is generally efficient.

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 and no output schema, the description provides adequate but incomplete context. It explains the core behavior (returns search URLs) and integration requirement (use WebFetch), which addresses the most critical gap. However, for a web search tool with 3 parameters and many sibling alternatives, it should ideally provide more guidance about when to choose this versus specialized search tools, and more details about the URL format or result structure.

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 100%, so the schema already fully documents all three parameters (query, engine, count) with descriptions and defaults. The description doesn't add any parameter semantics beyond what's in the schema - it doesn't explain query formatting, engine selection criteria, or count limitations. Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 performs '通用网络搜索' (general web search) and lists specific search engines (Google/Bing/百度/搜狗), providing a specific verb+resource. However, it doesn't explicitly distinguish this general web search tool from its many specialized sibling tools (like ai_search_github, ai_search_stackoverflow, etc.) that search specific 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 description provides clear context about when to use this tool - for general web searches across major search engines. The 【重要】 section explicitly states this tool returns search URLs that should be accessed with WebFetch tool, providing important usage guidance. However, it doesn't explicitly say when NOT to use it versus the specialized sibling tools for platform-specific searches.

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 full burden and does well by disclosing key behavioral traits: it returns URLs rather than direct content, and requires a secondary tool (WebFetch) to access actual results. It doesn't mention rate limits, authentication needs, or error handling, but provides crucial workflow information that isn't obvious from the schema alone.

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 perfectly front-loaded with the core purpose, followed by critical usage instructions. Every sentence earns its place - the first establishes what it does, the second provides essential workflow guidance. No wasted words or 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?

For a search tool with no output schema, the description provides excellent context about what the tool returns (URLs) and how to use those results. It covers the essential workflow despite the lack of structured output documentation. The only minor gap is not explicitly mentioning what happens with empty results or error conditions.

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 100%, providing good documentation for both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (query for keywords, platform with enum values). This meets the baseline expectation when schema coverage is complete.

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 specific verb ('搜索' - search) and resource ('微信开发者文档' - WeChat developer documentation), explicitly distinguishing it from sibling tools like ai_search_aliyun_docs or ai_search_tencent_docs by focusing exclusively on WeChat documentation. The emoji and Chinese title reinforce the specific domain.

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 guidance with 【重要】 (Important) notation, specifying that this tool returns URLs and Claude Code should use WebFetch to access real results. This clearly indicates when to use this tool (for WeChat docs search) and what to do with its output, distinguishing it from general search tools in the sibling list.

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