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

The description mentions parallel processing and faster performance, but lacks details on rate limits, error handling, or response format. With no annotations, more behavioral context would be beneficial.

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: a single sentence with key information front-loaded. 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 batch fetch tool with no output schema and no annotations, the description is moderately complete. It explains the basic purpose and parallelism, but lacks details on limits, errors, or output 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?

The parameter schema already describes the 'urls' parameter as a URL array. The description adds no additional meaning beyond that, such as URL format or constraints. Schema coverage is 100%, but the description does not compensate.

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 function: batch extraction of multiple web pages with parallel processing. It distinguishes itself from siblings like fetch_url (single URL) and 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 implies usage for multiple URLs, but does not explicitly state when to use vs. alternatives like fetch_url for single URLs or search tools for finding content. No when-not-to-use guidance.

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

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

No annotations are provided, so the description bears full burden for behavioral disclosure. It states the output (title and body) but does not disclose error handling, rate limits, rendering behavior, authentication needs, or what happens with invalid URLs. This is minimal transparency for a web fetching 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?

The description is a single, front-loaded sentence with no extraneous content. Every word is essential, achieving maximum conciseness while conveying purpose and output.

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 output schema), the description adequately covers its core function and return value. However, it could be more complete by briefly noting limitations or exceptions (e.g., 'if the page is not accessible, an error is returned'). Still, for a basic fetch tool, it is mostly sufficient.

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% (url parameter has a clear description). The tool description adds the overall purpose and output format, but does not provide additional semantic detail about the parameter beyond what the schema already says ('要提取内容的网页 URL'). Baseline 3 is appropriate as the description does not enhance parameter understanding further.

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 '提取' (extract) and resource '单个网页内容' (single web page content), and specifies output '标题和正文' (title and body). It effectively distinguishes from siblings like batch_fetch_urls (which handles multiple URLs) and search_and_extract (which likely involves searching first).

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 explicit guidance on when to use this tool versus alternatives. While the description implies single-URL scope, it doesn't mention that batch operations should use batch_fetch_urls or that search scenarios should use search_and_extract. The agent is left to infer 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, and the description does not disclose any behavioral traits such as authentication requirements, rate limits, error handling, or the nature of the returned results. The schema provides constraints for maxResults, but the description adds no behavioral 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 extremely concise with a single sentence. While it lacks detail, it is not verbose. For a simple tool, the brevity is acceptable, though it could benefit from additional context.

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 no output schema, so the description should clarify what the return value contains (e.g., titles, URLs, snippets). It does not. Also, given the presence of sibling tools, the description should help the agent choose between them, which it does not.

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, describing both parameters with their types, defaults, and constraints. The description does not add any further meaning beyond the schema, so a 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?

The description states that the tool uses Google search and returns results, which is clear but vague. It does not specify the type of results (e.g., web pages, structured data) nor differentiate from sibling tools like 'search_and_extract' or 'search_by_image'.

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 offers no guidance on when to use this tool versus alternatives. It simply states its function without any context on appropriate use cases or limitations.

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 exist, so the description bears full burden. It mentions automatic extraction of 'full content' but omits behavioral traits like potential multiple HTTP requests, rate limiting, or memory/time costs, which are crucial for an agent to understand.

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 with no wasted words. It efficiently conveys the core purpose, though a bit more structure (e.g., listing steps) could improve 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 lack of output schema and sibling tools (google_search, fetch_url, etc.), the description fails to explain return value structure, highlight limitations, or clarify differentiation, leaving agents to infer too much.

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%; both parameters (query, maxResults) have descriptions. The description adds 'extract result count (default 3)' for maxResults, but this largely mirrors the schema. No additional constraints or usage nuances are provided.

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 a search and automatically extracts the full content of the top N results. It distinguishes the combined action but does not explicitly differentiate from siblings like google_search (snippets only) or fetch_url (single URL).

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, such as for contexts requiring full-page extraction versus snippet-only searches. Prerequisites or exclusions are absent.

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 bears full transparency burden. It discloses dual-platform search and AI analysis, but lacks details on authentication requirements, error handling, rate limits, or what happens when the image is invalid. It adds moderate behavioral context beyond the schema.

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 packs key information but uses Chinese with parentheticals. It is front-loaded and efficient, though structure could be improved for machine parsing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so description should explain return value structure. It mentions '相似产品参考图' but not format or fields. For a tool with 3 parameters, the description is adequate but lacks return specification.

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 3 parameters. The description does not add meaning beyond what the schema already provides for image_path, max_results, and enable_ai_analysis. Baseline of 3 is appropriate as schema already documents parameters adequately.

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 function: uploading a product image to search for similar reference images across Google and Pinterest, with AI visual analysis and quality scoring. It distinguishes clearly from sibling tools like google_search or fetch_url, which are text-based URL fetch 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?

No explicit guidance on when to use this tool versus alternatives. The description implies usage for image-based product search but does not mention exclusions, prerequisites, or when to choose a sibling tool instead.

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