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 mentions that the tool '调用模型进行回答' (calls a model for answering) but doesn't specify what model is used, whether there are rate limits, authentication requirements, response formats beyond '模型的回答' (model's answer), or any error conditions. This leaves critical behavioral aspects undocumented.

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 a clear structure: a brief purpose statement followed by Args and Returns sections. However, the purpose statement is overly vague, and the parameter documentation is minimal, which slightly reduces its effectiveness despite the efficient format.

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 simplicity (1 parameter, no annotations, but with an output schema), the description is minimally complete. The output schema likely covers return values, reducing the need for detailed output explanation. However, for a model-calling tool, more context about the model type, capabilities, or limitations would be beneficial to fully understand its use.

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 description adds minimal value beyond the input schema. It states 'prompt: 要发送给模型的提示词' (prompt: the prompt to send to the model), which slightly clarifies the parameter's purpose but doesn't provide format requirements, length constraints, or examples. With 0% schema description coverage and only 1 parameter, this is adequate but leaves room for improvement.

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 '调用模型进行回答' (call model for answering) is a tautology that essentially restates the tool name 'call_model' without providing specific details about what kind of model is being called or what type of answers it produces. While it mentions '回答' (answers), it doesn't distinguish this tool from sibling tools like 'summarize_content' or 'topic_based_summary' that also involve model processing.

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

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

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention any specific context, prerequisites, or exclusions. Given sibling tools like 'summarize_content' and 'topic_based_summary' that appear to serve similar purposes, the lack of differentiation is a significant gap.

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 burden. It mentions a character limit ('限制2k字符') which is useful behavioral context, but doesn't disclose other important traits like error handling, performance characteristics, authentication needs, or rate limits. For a file-reading tool with no annotations, this leaves significant 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 efficiently structured with a clear purpose statement, parameter explanations, and return value indication. Every sentence serves a purpose, though the formatting with 'Args:' and 'Returns:' sections could be more integrated into natural language flow.

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 an output schema (though not shown), the description doesn't need to detail return values. However, for a file-processing tool with no annotations and 2 parameters, it should provide more behavioral context about file access permissions, supported PDF formats, or error conditions. The character limit hint is helpful but insufficient for full completeness.

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

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

With 0% schema description coverage, the description must compensate, and it does by explaining both parameters: 'filepath: PDF文件路径' (PDF file path) and 'target_ratio: 目标压缩比例，0.1-1.0之间' (target compression ratio between 0.1-1.0). This adds meaningful context beyond the bare schema, though it doesn't explain how the ratio affects summarization quality or length.

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: '读取PDF文件并总结内容' (read PDF file and summarize content). It specifies the resource (PDF files) and verb (read and summarize), but doesn't explicitly differentiate from sibling tools like 'read_and_summarize_text_file' or 'summarize_content', which handle similar operations on different inputs.

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 sibling tools like 'read_and_summarize_text_file', 'summarize_content', and 'summarize_webpage' available, there's no indication of when PDF-specific summarization is preferred over general content summarization 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 mentions the character limit constraint and that it returns a summary, but lacks critical behavioral details: whether it handles large files, what happens with unsupported formats, error conditions, performance characteristics, or authentication requirements. For a file-reading tool with zero annotation coverage, this leaves significant 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 efficiently structured with a clear purpose statement followed by Args and Returns sections. The Chinese text is concise and front-loaded with the core functionality. However, the character limit note in parentheses feels slightly tacked on rather than integrated, and the overall description could be more polished in its flow.

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 (file reading + summarization), no annotations, and an output schema that presumably documents the return value, the description is minimally adequate. It covers the basic what and how but lacks important context about limitations, error handling, and differentiation from similar tools. The presence of an output schema helps, but the description should do more to guide effective use.

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 description adds meaningful context for both parameters: it explains that 'filepath' is for text files (beyond just being a string path) and that 'target_ratio' controls compression (0.1-1.0 range). However, with 0% schema description coverage, the schema provides only basic type information. The description compensates somewhat but doesn't fully explain parameter interactions or provide examples of effective target_ratio values.

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: '读取txt等格式的文本文件并总结内容' (read text files like txt and summarize content). It specifies the resource (text files) and action (read and summarize). However, it doesn't explicitly differentiate from sibling tools like 'summarize_content' or 'read_and_summarize_pdf_file', which reduces clarity about when to choose this specific tool.

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 minimal usage guidance. It mentions file format support (txt等格式) and a character limit (限制2k字符), but offers no explicit advice on when to use this tool versus alternatives like 'summarize_content' or 'read_and_summarize_pdf_file'. There's no mention of prerequisites, typical use cases, or comparisons with sibling 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions optional file saving but lacks critical details: whether the tool performs authentication, respects robots.txt, handles rate limits, manages errors, or what format the scraped content takes (e.g., raw HTML, cleaned text). For a web scraping tool with zero annotation coverage, this leaves significant behavioral 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 well-structured and concise, using a clear header format with Args and Returns sections. Each sentence earns its place by defining purpose and parameters efficiently. It could be slightly more front-loaded by stating the core purpose first, but overall it avoids redundancy and waste.

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 (web scraping with file output), no annotations, and an output schema present (which handles return values), the description is minimally adequate. It covers the basic operation and parameters but lacks context on scraping behavior, error handling, or integration with siblings. The output schema relieves the description from detailing return values, but more behavioral context is needed for full completeness.

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

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

The description adds basic semantics for both parameters: 'url: 要抓取的网页URL' (URL of the webpage to scrape) and 'save_to_file: 是否保存内容到txt文件' (whether to save content to txt file). With 0% schema description coverage, this compensates somewhat by explaining what each parameter does. However, it doesn't provide format details (e.g., URL validation) or file path behavior, keeping it at the baseline 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: '抓取网页内容' (scrape webpage content) with an optional '保存为txt文件' (save as txt file). It specifies the verb (scrape) and resource (webpage content), distinguishing it from sibling tools like summarize_webpage or topic_based_summary that process rather than extract content. However, it doesn't explicitly differentiate from hypothetical scraping alternatives, keeping it at 4 instead of 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like summarize_webpage for summarization tasks or other content extraction methods. There's no context about prerequisites, limitations, or typical use cases, leaving the agent with minimal usage direction.

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 of behavioral disclosure. It mentions the default ratio (20%) and parameter ranges (0.1-1.0), but lacks critical details: it doesn't specify the summarization method (e.g., extractive vs. abstractive), quality expectations, handling of different content types, or potential limitations like length constraints. This leaves significant gaps in understanding how the tool behaves.

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 and well-structured: a single sentence states the purpose, followed by clearly labeled sections for Args and Returns. Every sentence earns its place, with no redundant information, making it easy to parse and understand quickly.

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 annotations, but with an output schema), the description is partially complete. It covers the basic purpose and parameters adequately, and the output schema handles return values. However, it lacks behavioral details (e.g., summarization approach, error cases) and usage context relative to siblings, leaving room for improvement in guiding the agent effectively.

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 description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'content' is '要总结的内容' (content to summarize) and 'target_ratio' is '目标压缩比例，0.1-1.0之间' (target compression ratio between 0.1-1.0), including the default value of 0.2. This compensates well for the schema's lack of descriptions, though it doesn't detail format expectations for 'content'.

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: '将任意内容总结为指定比例的长度' (summarize any content to a specified ratio length). It specifies the verb '总结' (summarize) and the resource '任意内容' (any content), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'summarize_webpage' or 'topic_based_summary', 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 no guidance on when to use this tool versus alternatives. With siblings like 'summarize_webpage' and 'topic_based_summary' available, it fails to indicate scenarios where this general-purpose summarizer is preferred over more specialized tools, leaving the agent without usage context.

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 burden. It mentions scraping and summarizing with a default ratio, but lacks details on behavioral traits such as error handling, rate limits, authentication needs, or what happens if the URL is invalid. This is inadequate for a tool that performs web operations.

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, with the core purpose stated first, followed by parameter and return details in a structured format. It avoids unnecessary elaboration, though the Chinese text might require translation for some agents.

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, 2 parameters with 0% schema coverage, and an output schema exists (so return values are covered), the description is moderately complete. It explains parameters and purpose but lacks behavioral context and usage guidelines, making it adequate but with gaps.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining 'url' as the webpage URL to scrape and summarize, and 'target_ratio' as the compression ratio between 0.1-1.0, with a default of 20%. This clarifies beyond the schema's basic types and titles.

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: '抓取网页内容并总结为指定比例的长度' (scrape webpage content and summarize to a specified ratio length). It specifies the verb (scrape and summarize) and resource (webpage content), though it doesn't explicitly differentiate from siblings like 'scrape_webpage' or 'summarize_content'.

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

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

No guidance is provided on when to use this tool versus alternatives. With siblings like 'scrape_webpage', 'summarize_content', and 'topic_based_summary', the description lacks context on use cases, exclusions, or comparisons, leaving the agent without direction.

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 burden. It mentions the 2k character limit, which is useful behavioral context. However, it doesn't disclose other important traits like whether this is a read-only operation, potential rate limits, error conditions, or how 'most relevant' is determined. For a tool with no annotation coverage, this leaves significant behavioral 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 appropriately concise with three sentences that each serve a purpose: stating the tool's function, listing parameters, and describing the return value. It's front-loaded with the core purpose. The bilingual formatting is slightly distracting but doesn't significantly impact 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 (2 parameters, topic-based summarization), the description covers the essential purpose and parameters. The presence of an output schema means the description doesn't need to explain return values in detail. However, with no annotations and sibling tools present, more usage guidance 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 0%, so the description must compensate. It lists both parameters ('content' and 'query') with brief explanations, adding meaning beyond the bare schema. However, it doesn't provide format expectations, constraints, or examples for either parameter, leaving some semantic gaps despite covering both parameters.

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: '基于给定资料和查询主题，返回最相关的内容总结' (based on given content and query topic, returns the most relevant content summary). It specifies the verb '返回' (return) and resource '内容总结' (content summary), though it doesn't explicitly differentiate from sibling tools like 'summarize_content' or 'summarize_webpage' beyond mentioning the topic-based aspect.

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 sibling tools like 'summarize_content' and 'summarize_webpage' available, there's no indication of when this topic-based approach is preferred over general summarization tools or when it should be avoided.

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