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. While it mentions the output format (base64 JPEG), it doesn't cover important aspects like performance characteristics (e.g., timeouts, size limits), error handling, authentication requirements, or rate limits. For a tool that interacts with external websites, this is a significant gap in transparency.

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 - two sentences that efficiently communicate the tool's purpose and usage scenarios. Every word earns its place with no redundancy 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 tool with 3 parameters, 100% schema coverage, but no annotations or output schema, the description provides adequate purpose and usage context. However, it lacks important behavioral details (performance, errors, limits) that would be crucial for an agent to use this tool effectively in production scenarios.

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

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

The schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description doesn't add any additional parameter semantics beyond what's in the schema (e.g., it doesn't explain trade-offs between firstScreenOnly options or when to use return_url). Baseline 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 specific action ('capture screenshots'), resource ('web pages'), and output format ('base64 encoded JPEG format'). It distinguishes from siblings like 'read_url' or 'parallel_read_url' by focusing on visual capture rather than text extraction or parallel 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?

The description provides clear context for when to use the tool ('visually inspect a website, take a snapshot for analysis, or show users what a webpage looks like'), which helps differentiate it from text-based or search-oriented siblings. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the siblings.

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

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

No annotations are provided, so the description carries the full burden. It discloses the algorithm ('Jina CLIP v2 embeddings and submodular optimization') and the automatic k-selection behavior, but lacks details on performance characteristics (e.g., rate limits, computational requirements), error handling, or output format. For a tool with no annotations, this is a moderate gap.

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-loaded with the core purpose and followed by usage guidance. Every sentence earns its place by providing essential information without redundancy, making it highly efficient and well-structured.

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 moderately complete. It covers the tool's purpose and usage well but lacks details on behavioral traits (e.g., performance, errors) and output format. For a tool with 2 parameters and algorithmic complexity, more context would be beneficial.

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 baseline is 3. The description adds value by explaining the purpose of deduplication ('semantically unique images') and the optimization approach, which provides context beyond the schema's technical parameter descriptions. However, it doesn't elaborate on parameter interactions or constraints.

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 verbs ('Get top-k semantically unique images') and resources ('URLs or base64-encoded images'), using technical details ('Jina CLIP v2 embeddings and submodular optimization'). It distinguishes from sibling tools like 'deduplicate_strings' by focusing on images rather than strings.

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 states when to use this tool: 'Use this when you have many visually similar images and want the most diverse subset.' This provides clear context for application, though it doesn't specify when not to use it or name alternatives, which is acceptable given the specificity of the use case.

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 discloses the method ('Jina embeddings and submodular optimization') and the automatic k-selection behavior, which adds valuable context beyond basic functionality. However, it doesn't mention performance characteristics, rate limits, error conditions, or output format details, leaving some behavioral aspects unclear.

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 functionality in the first sentence, followed by usage guidance and examples. Every sentence earns its place by adding distinct value: the first explains what it does, the second when to use it, and the third provides concrete use cases. No wasted words or 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's moderate complexity (semantic processing with optional parameters), no annotations, and no output schema, the description does well by explaining the algorithm and use cases. However, it doesn't describe the return format or what happens when k is not provided beyond 'automatically finds optimal k,' leaving some gaps in completeness for an agent invoking the 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema descriptions. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in 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 with specific verbs ('get top-k semantically unique strings') and resources ('from a list using Jina embeddings and submodular optimization'). It distinguishes itself from sibling tools like 'deduplicate_images' by specifying it works with strings rather than images, and from 'sort_by_relevance' by focusing on semantic diversity rather than relevance 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 states when to use this tool ('when you have many similar strings and want to select the most diverse subset that covers the semantic space') and provides three specific use cases ('removing duplicates, selecting representative samples, or finding diverse content'). It doesn't mention when not to use it, but the clear context and alternatives implied by sibling tools make this sufficient for a top 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?

No annotations are provided, so the description carries full burden. It mentions the tool uses 'an up-to-date query expansion model' and returns 'multiple expanded queries', but lacks details on behavioral traits like rate limits, authentication needs, response format, or potential limitations. The description is functional but insufficient for a mutation-like operation (query expansion) without annotation support.

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 three sentences that each add value: stating the tool's function, explaining the input/output, and providing usage context. It's front-loaded with the core purpose, though the third sentence could be slightly more concise.

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 moderately complete for a single-parameter tool. It covers the basic purpose and usage, but lacks details on return values (e.g., format of expanded queries), error handling, or performance characteristics, which would be helpful for an agent to use it 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?

Schema description coverage is 100%, so the schema already documents the single 'query' parameter with examples. The description adds marginal value by implying the query should be for search purposes (e.g., 'machine learning', 'climate change'), but doesn't provide additional syntax, constraints, or format details beyond what the schema specifies.

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: 'Expand and rewrite search queries based on an up-to-date query expansion model' and 'returns multiple expanded queries'. It specifies the verb (expand/rewrite) and resource (search queries), but doesn't explicitly differentiate from sibling tools like 'parallel_search_web' or 'search_web' beyond mentioning 'more diversed and deeper searches'.

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: 'Useful for improving deep research results by searching broader and deeper'. It suggests when to use it (for deeper research) but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools, leaving some ambiguity about tool selection.

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 discloses the tool's behavior by stating it 'returns base64-encoded images of detected elements with metadata', which adds value beyond the input schema. However, it lacks details on error handling, rate limits, or authentication needs, leaving gaps for a tool with no annotation coverage.

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 front-loaded with the core purpose in the first sentence, followed by usage context and output details. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 return format ('base64-encoded images with metadata'). However, for a tool with 4 parameters and complex PDF processing, it lacks details on performance, limitations, or error cases, making it adequate but with clear 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 100%, so the schema already documents all parameters thoroughly. The description adds minimal semantic context by mentioning 'arXiv paper ID' and 'PDF URL' in the usage context, but does not provide additional meaning beyond what the schema specifies. Baseline 3 is appropriate given high 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 verb ('extract') and resources ('figures, tables, and equations from PDF documents'), specifying the method ('using layout detection'). It distinguishes from siblings like 'read_url' or 'search_arxiv' by focusing on visual element extraction rather than general reading or searching.

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 it ('Perfect for extracting visual elements from academic papers on arXiv or any PDF URL'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for PDFs with visual content but lacks exclusions.

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 and does so effectively. It describes the comprehensive analysis approach (examining HTTP headers, HTML metadata, Schema.org data, etc.), mentions the confidence scoring system, and specifies the return format (best guess timestamp and confidence level). It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial 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 perfectly structured and concise - two sentences that efficiently convey the tool's purpose, methodology, and return values. Every element earns its place: the first sentence explains what the tool does and how it works, the second specifies the output format. 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 single-parameter tool with no output schema, the description provides excellent context about the tool's behavior, methodology, and return format. It explains the comprehensive analysis approach and specifies what information will be returned (timestamp and confidence level). The only minor gap is the lack of explicit error handling or edge case 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% for the single 'url' parameter, so the schema already documents this parameter adequately. The description doesn't add significant meaning beyond what the schema provides - it mentions analyzing webpages but doesn't elaborate on URL format requirements, supported protocols, or edge cases beyond the schema's 'uri' format specification.

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 ('guess') and resource ('last updated or published datetime of a web page'), distinguishing it from sibling tools like 'read_url' or 'parallel_read_url' which focus on content extraction rather than timestamp inference. It explicitly defines the tool's unique function of analyzing multiple data sources to determine update times with confidence scoring.

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 - when you need to determine when a webpage was last updated or published. However, it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools, such as using 'read_url' for general content extraction instead of timestamp guessing.

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 mentions 'efficiently' and 'for best results... maximum 5 URLs for optimal performance' (implied rate/performance limits), but doesn't disclose error handling, authentication needs, or what 'clean content' specifically means. It adds some behavioral context but leaves 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 sized with three concise sentences. The first states the purpose, the second gives usage guidance, and the third provides context—all front-loaded with zero 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 no annotations and no output schema, the description is moderately complete for a tool with 2 parameters and 100% schema coverage. It covers purpose and usage but lacks details on output format, error cases, or deeper behavioral traits, leaving room for improvement.

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 baseline is 3. The description adds minimal parameter semantics beyond the schema—it emphasizes 'multiple URLs' and 'simultaneously' for the 'urls' parameter but doesn't explain 'timeout' or the nested options ('withAllLinks', 'withAllImages').

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 ('read multiple web pages in parallel to extract clean content efficiently'), identifies the resource ('web pages'), and distinguishes it from sibling tools like 'read_url' by emphasizing parallel processing and multi-URL capability.

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 ('for comparing content across multiple sources or gathering information from multiple pages at once') and mentions 'for best results, provide multiple URLs.' However, it doesn't explicitly state when NOT to use it or name alternatives like 'read_url' for single URLs.

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 for behavioral disclosure. It mentions 'optimal performance' with 'maximum 5 searches' and suggests 'best results' with diverse queries, but doesn't cover important behavioral aspects like error handling, rate limits, authentication needs, or what happens when timeout is reached. The description adds some context but 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 appropriately sized with three sentences. The first sentence states the core purpose, the second provides usage guidance, and the third suggests query generation alternatives. It's front-loaded with the main functionality and has 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 parallel search tool with no annotations and no output schema, the description provides adequate basic context about purpose and usage. However, it lacks important details about return format, error conditions, and behavioral constraints that would be needed for complete understanding. The 100% schema coverage helps, but behavioral aspects are under-specified.

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 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions 'multiple search queries' which aligns with the 'searches' parameter, but provides no additional semantic context.

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: 'Run multiple arXiv searches in parallel for comprehensive research coverage and diverse academic angles.' It specifies the verb ('run multiple searches'), resource ('arXiv'), and scope ('in parallel'), though it doesn't explicitly differentiate from sibling 'search_arxiv' beyond the parallel 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 clear context for when to use this tool: for 'comprehensive research coverage and diverse academic angles' with 'multiple search queries.' It explicitly mentions an alternative tool ('expand_query') for generating queries, but doesn't specify when to use this versus the sibling 'search_arxiv' 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?

No annotations are provided, so the description carries the full burden. It mentions 'for best results' and 'optimal performance' with max 5 searches, which adds some behavioral context, but doesn't disclose critical details like rate limits, authentication needs, error handling, or what the output looks like (no output schema exists). The description is adequate but lacks depth for a parallel search 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 appropriately sized with three sentences. It's front-loaded with the core purpose, followed by usage guidance and alternatives. Every sentence adds value, though the second sentence could be slightly more concise.

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 complexity (parallel searches with multiple parameters) and no annotations or output schema, the description is minimally complete. It covers the purpose and basic usage but lacks details on behavioral traits, output format, and explicit differentiation from sibling tools. It's adequate but has clear gaps for an agent to use it 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no specific parameter information beyond what's in the schema, but it does imply the purpose of the 'searches' array ('multiple search queries that explore different research angles'). Baseline 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's purpose: 'Run multiple SSRN searches in parallel for comprehensive social science research coverage and diverse academic angles.' It specifies the verb ('run'), resource ('SSRN searches'), and scope ('in parallel'), though it doesn't explicitly differentiate from the sibling 'search_ssrn' tool, which appears to be a single-search version.

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 on when to use this tool: for 'comprehensive social science research coverage and diverse academic angles' and suggests 'provide multiple search queries that explore different research angles and methodologies.' It also mentions an alternative tool ('expand_query') for generating queries, but doesn't explicitly state when to use this vs. the sibling 'search_ssrn' tool for single 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. It discloses the parallel execution behavior and mentions 'optimal performance' with max 5 searches, but doesn't cover error handling, rate limits, authentication needs, or what happens if searches fail. It 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 perfectly concise with three sentences that each earn their place: states purpose, provides usage guidance, and suggests query generation alternatives. It's front-loaded with the core functionality and wastes no 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 no annotations and no output schema, the description should do more to explain behavioral aspects like error handling, result format, or performance characteristics. While it covers purpose and usage well, it lacks completeness for a parallel execution tool with complex parameters and no structured output documentation.

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 parameters thoroughly. The description adds minimal value beyond the schema - it mentions providing 'multiple search queries' which aligns with the 'searches' array parameter, but doesn't explain parameter interactions or provide additional semantic context.

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 verbs ('run multiple web searches in parallel') and resource ('web searches'), distinguishing it from siblings like 'search_web' (singular) and 'parallel_search_arxiv' (different domain). It explicitly mentions 'comprehensive topic coverage and diverse perspectives' as the goal.

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: for 'comprehensive topic coverage and diverse perspectives' by running 'multiple search queries that explore different aspects of your topic.' It also names an alternative tool ('expand_query') for generating queries and distinguishes from single-search siblings.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool retrieves 'up-to-date contextual information' and implies it's a read operation without destructive effects. However, it lacks details on behavioral traits such as rate limits, authentication needs, data freshness guarantees, or error handling. The description adds value by specifying the types of context but doesn't fully compensate for the absence of 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 concise and well-structured, consisting of two sentences that efficiently convey purpose and usage. The first sentence explains what the tool does, and the second provides clear guidance on when to use it. There is no redundant or extraneous 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 complexity (simple context retrieval with no parameters) and the absence of annotations and output schema, the description is moderately complete. It covers the purpose and usage well but lacks details on behavioral aspects like response format, potential errors, or data sources. For a tool with no structured metadata, it should ideally include more about what information is returned and how it's structured.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately avoids discussing parameters, focusing instead on the tool's purpose and usage. This aligns with the baseline expectation for tools with no parameters, as there's nothing to compensate for.

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: to 'get up-to-date contextual information of the current session' for providing 'localized, time-aware responses.' It specifies the types of information retrieved (current time, user's location, network environment) and the goal (more relevant and personalized information). However, it doesn't explicitly differentiate from siblings like 'guess_datetime_url' or 'show_api_key,' which might provide overlapping context.

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: 'Use this when you need to know the current time, user's location, or network environment to give more relevant and personalized information.' This clearly states when to use the tool, including specific scenarios (e.g., for time-aware responses) and distinguishes it from siblings by focusing on session context rather than external data retrieval or processing.

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 'bypass paywalls' which implies potential access to restricted content, but doesn't address rate limits, authentication needs, error handling, or what happens with malformed URLs. For a tool that interacts with external websites, 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 sized with three sentences that each serve distinct purposes: stating the core function, providing use cases, and giving application scenarios. It's front-loaded with the main purpose. Minor improvement could come from more specific differentiation from sibling tools.

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/conversion), no annotations, and no output schema, the description is somewhat incomplete. It covers the 'what' and 'why' well but lacks details about return format, error conditions, performance characteristics, or how the markdown conversion handles different page structures. The absence of output schema means the description should ideally address what the tool returns.

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 complete parameter documentation. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline score of 3. It doesn't explain how 'withAllLinks' or 'withAllImages' affect the output format or provide examples of the structured data returned.

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 verbs ('extract and convert') and resources ('web page content to clean, readable markdown format'). It distinguishes itself from siblings like capture_screenshot_url (visual capture) and extract_pdf (PDF-specific extraction) by focusing on HTML-to-markdown conversion for general web 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?

The description provides clear usage context with examples ('articles, documentation, blog posts') and scenarios ('bypass paywalls, get structured data'). However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among siblings, such as when to use parallel_read_url for multiple URLs versus this tool's array capability.

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 for behavioral disclosure. It mentions the tool is 'perfect for finding research papers' but doesn't describe rate limits, authentication needs, pagination behavior, error conditions, or what the response format looks like. For a search 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 appropriately sized with two sentences. The first sentence states the purpose clearly, and the second provides usage examples. While efficient, the second sentence could be slightly more concise by reducing the list of examples, but overall it's well-structured and front-loaded.

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

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

Given no annotations and no output schema, the description provides adequate basic information about what the tool does and when to use it, but lacks details about behavioral traits, response format, and explicit differentiation from sibling tools. For a search tool with 3 parameters and no structured output documentation, this is minimally viable but has clear 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 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in 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 searches academic papers on arXiv with specific examples (research papers, scientific studies, etc.), but doesn't explicitly differentiate from sibling tools like parallel_search_arxiv or search_ssrn. It provides a specific verb ('search') and resource ('academic papers and preprints on arXiv repository'), making the purpose unambiguous.

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 guidelines with examples ('when researching scientific topics, looking for papers by specific authors, or finding the latest research'), but doesn't explicitly state when to use this tool versus alternatives like parallel_search_arxiv or search_ssrn. It gives context but lacks explicit comparison or exclusion criteria.

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 describes key behaviors: the tool searches across the web, returns images by default as small base64-encoded JPEGs, and offers an option to return URLs and metadata. However, it lacks details on rate limits, authentication needs, or potential costs, which are important for an agent to use the tool responsibly.

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 the core purpose and usage guidelines. All sentences earn their place by adding value, such as clarifying the return format and use cases. However, it could be slightly more concise by combining some of the use-case examples into a single phrase.

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 complexity of a 6-parameter tool with no annotations and no output schema, the description is somewhat complete but has gaps. It covers the purpose, usage, and return format well, but lacks details on error handling, pagination, or response structure, which would help an agent invoke the tool more effectively. The absence of an output schema increases the need for more descriptive context.

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, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning the default base64 encoding and URL/metadata option, but it does not provide additional context or examples for parameters like 'tbs' or 'location' that aren't already in 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's purpose with specific verbs ('Search for images across the web') and resources ('images'), distinguishing it from siblings like search_web or search_arxiv by focusing exclusively on visual content. It explicitly mentions the similarity to Google Images, which provides immediate context about the tool's function.

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 guidance on when to use this tool ('when you need to find photos, illustrations, diagrams, charts, logos, or any visual content') and gives examples of use cases ('illustrate concepts, locating specific pictures, or discovering visual resources'). However, it does not explicitly state when NOT to use it or mention alternatives among the sibling tools, such as search_web for general web 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 for behavioral disclosure. It describes what content is searched (Jina AI news/blog posts) and the purpose (finding documentation, tutorials, etc.), but doesn't disclose operational traits like rate limits, authentication needs, pagination behavior, or error handling. The description adds useful context about content scope but lacks technical 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?

The description is efficiently structured in two sentences with zero waste. The first sentence establishes purpose and scope, the second provides usage context. Every phrase adds value without redundancy, 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?

For a search tool with 3 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate purpose and usage context but lacks behavioral details about how results are returned, formatted, or limited. It's complete enough for basic understanding but would benefit from information about result structure or operational constraints given the absence of output schema.

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. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions search terms generally but doesn't provide additional syntax, format, or usage guidance for parameters. Baseline 3 is appropriate 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 specific action ('Search Jina AI news and blog posts') and resource ('at jina.ai/news'), distinguishing it from sibling tools like search_arxiv or search_web by specifying the exclusive Jina AI content source. It explicitly lists the types of content covered (documentation, tutorials, announcements, deep-dives).

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 ('to find official Jina documentation, tutorials, product announcements, and technical deep-dives'), but doesn't explicitly state when NOT to use it or name specific alternatives among siblings. It implies usage for Jina-specific content versus general web searches, though not with explicit exclusions.

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 mentions the tool is 'perfect for finding research papers' and covers subject domains, but doesn't disclose behavioral traits like rate limits, authentication needs, pagination, or response format. The description adds some context about what can be searched but lacks operational 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?

The description is appropriately sized with two sentences that are front-loaded: the first states the core purpose, and the second provides usage guidelines. Every sentence adds value, though it could be slightly more concise by combining some phrases.

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 (3 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers purpose and usage well but lacks details on behavioral traits and output format, which are important for a search tool with no annotations or output schema.

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 detailed documentation for all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 without compensating or adding extra meaning.

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 verbs ('search academic papers and preprints') and identifies the target resource ('SSRN/Social Science Research Network'). It distinguishes from siblings by specifying the academic focus and subject domains (social sciences, economics, law, etc.), unlike generic search tools like search_web or search_images.

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 states when to use this tool: 'when researching social science topics, looking for working papers, or finding the latest research in business and economics fields.' It also implies differentiation from siblings by specifying the academic/SSRN focus, though it doesn't name specific 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 are provided, so the description carries full burden. While it mentions 'current information' and 'up-to-date,' it doesn't disclose rate limits, authentication requirements, result format, pagination behavior, or error handling. For a web search 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 efficiently structured with two sentences: the first states purpose, the second provides usage guidelines. Every phrase adds value without redundancy. It could be slightly more concise by combining some clauses, but overall it's well-organized and front-loaded.

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

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

For a web search tool with 6 parameters, 100% schema coverage, but no output schema or annotations, the description adequately covers purpose and usage. However, it lacks details about return format, error conditions, or behavioral constraints that would help an agent use it effectively, making it minimally complete.

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

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

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds no parameter-specific information beyond implying general search functionality. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

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 explicitly states the action ('Search the entire web') and resources ('current information, news, articles, and websites'), distinguishing it from siblings like search_images or search_arxiv that target specific content types. It clearly defines the tool's scope as web-wide searching for various content formats.

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 scenarios: 'when you need up-to-date information, want to find specific websites, research topics, or get the latest news.' It also distinguishes from siblings by not mentioning image, academic, or URL-specific searches, implicitly guiding users toward alternatives like search_images or search_arxiv for those needs.

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 states the tool returns sensitive authentication data (a bearer token), which hints at security implications, but doesn't disclose behavioral traits like whether this requires specific permissions, if the token is masked or exposed in full, or any rate limits. The description adds some context ('used to debug') but lacks critical details for a security-sensitive 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 a single, efficient sentence that front-loads the core action ('Return the bearer token') and adds purpose ('used to debug'). There is zero waste, and every word contributes to understanding the tool's function.

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 complexity (simple, no parameters) and lack of annotations/output schema, the description is minimally complete: it explains what the tool does and its purpose. However, for a tool handling sensitive authentication data, it should ideally include more security or usage warnings to fully inform the agent, leaving some gaps in context.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't discuss parameters, which is appropriate, earning a baseline score of 4 for adequately handling the lack of parameters without unnecessary elaboration.

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: 'Return the bearer token from the Authorization header of the MCP settings' with the specific goal 'which is used to debug.' It uses a specific verb ('Return') and identifies the resource ('bearer token'), though it doesn't explicitly distinguish from siblings, which are unrelated debugging/utility 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 minimal guidance: it implies usage for debugging purposes but offers no explicit when-to-use or when-not-to-use advice. It doesn't mention alternatives or prerequisites, leaving the agent to infer context from the tool's name and description alone.

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 mentions the tool uses the 'Jina Reranker API' and describes its purpose, but lacks details on rate limits, authentication needs, error handling, or what the output looks like (since no output schema exists). The description is accurate but insufficient for full 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by usage guidelines and examples. Every sentence adds value without redundancy, making it efficient and well-structured.

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 (3 parameters, no annotations, no output schema), the description is somewhat complete but has gaps. It explains the purpose and usage well but lacks details on behavioral aspects like output format, error cases, or API constraints, which are important for a tool with no annotations or output schema.

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 parameters thoroughly. The description doesn't add any additional meaning beyond what's in the schema (e.g., it doesn't explain parameter interactions or provide examples). Baseline score of 3 is appropriate as 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 specific action ('Rerank a list of documents by relevance') and resource ('documents'), using the Jina Reranker API. It distinguishes this tool from siblings like 'deduplicate_strings' or 'search_web' by focusing on reranking rather than searching or deduplication.

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 states when to use this tool: 'Use this when you have multiple documents and want to sort them by how well they match a specific query or topic.' It also provides alternative use cases ('Perfect for document retrieval, content filtering, or finding the most relevant information from a collection'), though it doesn't explicitly name sibling alternatives.

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