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 output format (base64 JPEG) and general use cases but lacks details on potential behavioral traits like rate limits, authentication needs, error conditions, or performance implications (e.g., timeouts for slow-loading pages). The description is functional but insufficient for a mutation-like tool (capturing external resources) 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 concise and well-structured with two sentences: the first states the core functionality and output format, and the second provides usage guidelines. Every sentence adds value without redundancy, making it easy to parse and front-loaded with essential information.

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

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

Given the complexity of a screenshot tool (involving external web interactions) and the absence of both annotations and an output schema, the description is moderately complete. It covers purpose and usage but lacks details on behavioral aspects like error handling or output specifics (e.g., what the base64 string or URL looks like). This leaves gaps that could hinder effective tool invocation by an AI agent.

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

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

Schema description coverage is 100%, so the schema fully documents all three parameters (url, firstScreenOnly, return_url) with clear descriptions. The description adds no additional parameter semantics beyond what's in the schema, but this is acceptable given the high coverage, resulting in a baseline score of 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 with specific verbs ('capture high-quality screenshots') and resources ('web pages'), specifying the output format ('base64 encoded JPEG format'). It distinguishes itself from sibling tools like 'read_url' or 'search_web' by focusing on visual capture rather than text extraction 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 usage contexts ('visually inspect a website', 'take a snapshot for analysis', 'show users what a webpage looks like'), giving practical scenarios for when to use this tool. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among siblings, such as 'read_url' for text content instead of screenshots.

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 traits: it's a read-only operation (implied by 'get'), uses semantic analysis via embeddings, and includes optimization logic. However, it lacks details on performance aspects like rate limits or error handling, which would be helpful for a tool with computational complexity.

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 and usage guidelines in the second. Both sentences earn their place by adding essential information without redundancy, making it efficient and well-structured for quick comprehension.

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 (involving embeddings and optimization) and no output schema, the description is mostly complete. It covers purpose, usage, and high-level behavior, but could benefit from mentioning output format (e.g., returned structure) or limitations to fully compensate for the lack 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 documents both parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'top-k' and 'semantically unique,' but doesn't provide additional syntax or format details. This meets the baseline for 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 tool's purpose with specific verbs ('get top-k semantically unique images') and resources ('images (URLs or base64-encoded)'), and distinguishes it from sibling tools by specifying its unique function of deduplication using Jina CLIP v2 embeddings and submodular optimization, unlike other tools like search_images or deduplicate_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: 'when you have many visually similar images and want the most diverse subset.' This provides clear context and distinguishes it from alternatives like search_images (for finding images) or deduplicate_strings (for text deduplication), making it easy for an agent to select appropriately.

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 the tool's behavior: using Jina embeddings and submodular optimization for semantic deduplication, returning top-k results, and automatically determining optimal k if not provided. However, it doesn't mention performance characteristics like computational complexity or rate limits.

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: three sentences that each earn their place. The first states the core functionality, the second provides usage guidelines, and the third lists specific applications. No wasted words, front-loaded with essential information.

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

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

For a tool with no annotations and no output schema, the description provides good context about what the tool does and when to use it. However, it doesn't describe the return format or what 'semantically unique' means in practice. Given the complexity of semantic deduplication, more detail about the output would be helpful.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds some context about the 'k' parameter's automatic optimization behavior, but doesn't provide additional semantic meaning beyond what's in the schema descriptions. This meets the baseline for 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 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 siblings like deduplicate_images by focusing on strings rather than images, and from other tools by its semantic deduplication approach.

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 differentiates from siblings by not overlapping with their domains (e.g., images, web search).

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

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

No annotations are provided, so the description carries full burden. It mentions the tool 'returns multiple expanded queries' but lacks details on behavioral traits such as rate limits, authentication needs, response format, or potential side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, with the core purpose stated first. However, the second sentence could be more concise (e.g., 'diversed' is misspelled as 'diversed'), and some phrasing is slightly redundant, though overall it avoids 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?

Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is partially complete. It covers the purpose and usage context but lacks details on behavioral aspects and output format. Without annotations or output schema, more information on what 'multiple expanded queries' entails would improve completeness.

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

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

Schema description coverage is 100%, with the parameter 'query' well-documented in the schema. The description adds minimal value beyond the schema by implying the query is 'initial' and used for expansion, but does not provide additional syntax, format, or constraints. Baseline 3 is appropriate as the schema handles parameter documentation 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 purpose with specific verbs ('expand and rewrite search queries') and resource ('search queries'), distinguishing it from sibling tools like search_web or search_arxiv by focusing on query expansion rather than direct searching. It explicitly mentions using 'an up-to-date query expansion model' for transformation.

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 ('useful for improving deep research results by searching broader and deeper'), but does not explicitly state when not to use it or name specific alternatives among siblings. It implies usage for enhancing search effectiveness without direct 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. It describes the core functionality and output format ('Returns base64-encoded images of detected elements with metadata'), but lacks details about error conditions, rate limits, authentication requirements, or what happens with malformed PDFs. The description adds value but leaves 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 perfectly front-loaded with the core purpose in the first clause, followed by usage context and output details. Every sentence earns its place with zero wasted words, making it highly efficient and scannable.

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 (4 parameters, no output schema, no annotations), the description covers the core purpose and output format adequately but lacks details about error handling, performance characteristics, or limitations. It's complete enough for basic understanding but leaves operational questions unanswered.

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 4 parameters. The description mentions 'arXiv paper ID' and 'PDF URL' which aligns with the schema, but adds no additional parameter semantics beyond what the schema provides. The baseline score of 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('extract figures, tables, and equations'), the resource ('PDF documents'), and the method ('using layout detection'). It distinguishes from sibling tools by focusing on PDF content extraction rather than searching, reading, or processing other data types.

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 ('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 specific alternatives among the sibling tools for different PDF-related tasks.

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 the tool's approach by listing multiple data sources examined (e.g., HTTP headers, HTML metadata) and outputs (timestamp with confidence scores), giving a clear picture of its heuristic and probabilistic nature. However, it lacks details on error handling or performance characteristics.

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 following with key details on methods and outputs. It avoids redundancy, though it could be slightly more streamlined by combining some of the listed data sources into broader categories.

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 (heuristic datetime guessing) and lack of annotations or output schema, the description does a good job of explaining the process and return values. It covers the input parameter indirectly and outlines the output structure, though it could benefit from more explicit details on confidence score ranges or error cases.

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

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

The input schema has 100% description coverage, with the 'url' parameter well-documented in the schema itself. The description does not add any additional meaning or constraints beyond what the schema provides, such as URL format examples or validation rules, so it meets the baseline for 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 specific action ('guess the last updated or published datetime') and resource ('a web page'), distinguishing it from sibling tools like 'read_url' or 'capture_screenshot_url' that focus on different webpage interactions. It specifies the exact temporal information being extracted, 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 implies usage when temporal metadata about a webpage is needed, but does not explicitly state when to use this tool versus alternatives like 'read_url' (which might return raw content) or 'parallel_search_web' (which might provide search results). No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended.

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 efficiency benefits and the parallel nature of the operation, but doesn't disclose important behavioral traits like error handling, rate limits, authentication requirements, or what happens when URLs fail. It mentions 'optimal performance' with max 5 URLs but doesn't explain consequences of exceeding this limit.

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: first states the core functionality, second provides usage guidance, third gives concrete use cases. It's front-loaded with the main purpose and wastes no words while being comprehensive.

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 and expected outputs. While it covers purpose and usage well, it doesn't describe what the tool returns (clean content format, error responses, or structured data from links/images). For a tool with 2 parameters and no annotation coverage, the description is adequate but leaves gaps in behavioral transparency.

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 minimal parameter semantics beyond what's in the schema - it mentions 'multiple URLs' which aligns with the 'urls' parameter, but doesn't provide additional context about parameter usage or relationships beyond what the schema already 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 with specific verbs ('read multiple web pages in parallel', 'extract clean content efficiently') and distinguishes it from sibling tools like 'read_url' by emphasizing parallel processing and multi-URL capability. It explicitly mentions the resource ('web pages') and the efficiency benefit.

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: 'provide multiple URLs that you need to extract simultaneously' and gives concrete use cases ('comparing content across multiple sources', 'gathering information from multiple pages at once'). It distinguishes from single-URL alternatives by emphasizing parallel processing for multiple 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 the full burden of behavioral disclosure. It mentions the parallel execution nature and performance optimization ('maximum 5 searches for optimal performance'), which is useful. However, it doesn't disclose important behavioral aspects like rate limits, authentication requirements, error handling, or what the return format looks like (though there's no output schema). The description doesn't contradict any annotations since none exist.

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 three sentences. The first sentence states the core purpose, the second provides usage guidance, and the third offers a helpful tip about query generation. Every sentence adds value with zero wasted words, making it appropriately front-loaded and 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 moderate complexity (parallel execution of searches) and 100% schema coverage but no annotations or output schema, the description is adequate but has gaps. It explains the parallel nature and provides usage tips, but doesn't cover behavioral aspects like performance characteristics, error handling, or result format. For a tool executing parallel searches without output schema, more context about what to expect would be helpful.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema - it mentions 'multiple search queries' which aligns with the 'searches' array parameter, but doesn't provide additional context about parameter usage or constraints that aren't already in the schema descriptions. With high schema coverage, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run multiple arXiv searches in parallel for comprehensive research coverage and diverse academic angles.' It specifies the verb ('run'), resource ('arXiv searches'), and scope ('in parallel'), distinguishing it from the sibling 'search_arxiv' tool which presumably handles single searches. The description explicitly mentions the parallel execution 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 usage context: 'For best results, provide multiple search queries that explore different research angles and methodologies.' It also suggests using 'expand_query' to generate diverse queries. However, it doesn't explicitly state when NOT to use this tool (e.g., for single searches where 'search_arxiv' might be more appropriate) or provide direct alternatives among siblings.

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

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's a search operation (implied read-only), mentions 'optimal performance' with maximum 5 searches (implied rate/performance consideration), and suggests 'best results' with diverse queries. It doesn't explicitly mention authentication needs or rate limits, but provides practical usage 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 appropriately sized with three sentences, each earning its place: first states purpose, second provides usage guidance, third mentions alternative approaches. It's front-loaded with the core functionality and wastes no words on 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?

Given no annotations and no output schema, the description does well by covering purpose, usage guidelines, and behavioral context. It could be more complete by explicitly mentioning the read-only nature or expected return format, but for a search tool with good schema coverage, it provides sufficient context for 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?

Schema description coverage is 100%, so the baseline is 3. The description adds some value by explaining the purpose of providing 'multiple search queries' and mentioning 'diverse academic angles,' which gives context for the 'searches' array parameter, but doesn't add significant semantic detail beyond what the schema already documents about individual search 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 with specific verbs ('run multiple SSRN searches in parallel') and resource ('SSRN searches'), distinguishing it from siblings like 'search_ssrn' by emphasizing parallel execution for comprehensive coverage and diverse academic angles. It explicitly mentions social science research 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 guidance on when to use this tool ('for comprehensive social science research coverage and diverse academic angles'), how to use it ('provide multiple search queries'), and mentions an alternative tool ('expand_query') for generating diverse queries. It also suggests creating queries manually as another option.

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 'parallel' execution and 'optimal performance' with max 5 searches, which adds useful context beyond the schema. However, it doesn't disclose critical behavioral traits like rate limits, error handling, authentication needs, or what the output looks like (since no output schema exists). The description provides some operational context but leaves significant gaps for a tool that performs multiple external searches.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise and well-structured: three sentences that each earn their place. The first states the core purpose, the second provides usage guidance, and the third suggests a complementary tool. No wasted words, and the most important information (parallel execution for comprehensive coverage) is 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 the tool's complexity (parallel web searches with multiple configurable parameters) and the absence of both annotations and an output schema, the description is incomplete. While it explains the parallel nature and suggests query diversity, it doesn't address what results look like, how errors are handled, or performance considerations beyond the 5-search limit mentioned in the schema. For a tool with no output schema and no annotations, more behavioral context would be 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?

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. It mentions 'multiple search queries' which aligns with the 'searches' parameter, but provides no additional syntax, format, or semantic details. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run multiple web searches in parallel for comprehensive topic coverage and diverse perspectives.' It specifies the verb ('run'), resource ('web searches'), and key characteristic ('in parallel'), distinguishing it from sibling tools like 'search_web' (which presumably runs single searches). However, it doesn't explicitly contrast with 'parallel_search_arxiv' or 'parallel_search_ssrn' beyond mentioning 'web' 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 clear context for usage: 'For best results, provide multiple search queries that explore different aspects of your topic.' It also suggests an alternative tool: 'You can use expand_query to help generate diverse queries.' This gives practical guidance on when to use this tool (for parallel searches with diverse queries) and mentions a complementary tool. However, it doesn't explicitly state when NOT to use it (e.g., vs. single 'search_web' for simple queries).

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 specifies the types (time, location, network environment), which is useful behavioral context. However, it lacks details on data freshness, privacy implications, or response format, leaving gaps for a tool that handles sensitive session data. This is adequate but not comprehensive.

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 followed by usage guidance. Every word earns its place: the first sentence defines what the tool does, and the second specifies when to use it. There is no fluff or repetition, 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 the tool's low complexity (0 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, usage, and the types of information retrieved. However, without annotations or output schema, it could benefit from more detail on response structure or data sources, slightly limiting completeness for a session-context 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and usage. This meets the baseline of 4 for zero-parameter tools, as it adds value without redundancy.

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: 'Get up-to-date contextual information of the current session to provide localized, time-aware responses.' It specifies the verb ('get') and resource ('contextual information'), and distinguishes it from sibling tools by focusing on session metadata rather than search, extraction, or processing operations. However, it doesn't explicitly differentiate from all siblings (e.g., 'show_api_key' might also provide session info), keeping it at 4 rather than 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 explicit guidance on when to use the tool: 'Use this when you need to know the current time, user's location, or network environment to give more relevant and personalized information.' It clearly defines the triggering context (needing time, location, or network data for personalization) without needing to specify exclusions, as the context is distinct from sibling tools' purposes.

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 capabilities like bypassing paywalls and extracting structured data, which adds useful context beyond basic functionality. However, it lacks details on error handling, rate limits, authentication needs, or performance characteristics that would be important for an agent to use it effectively.

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. Every sentence adds value, such as use cases and capabilities. It could be slightly more concise by combining some phrases, but overall it avoids redundancy and maintains 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 complexity (web content extraction with multiple parameters) and no output schema, the description is moderately complete. It covers the purpose and use cases but lacks details on return values, error conditions, or limitations. With no annotations and no output schema, more behavioral context would improve completeness for agent usage.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain URL formats or the implications of withAllLinks/withAllImages). 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 with specific verbs ('extract and convert') and resources ('web page content'), distinguishing it from siblings like capture_screenshot_url (visual capture) or extract_pdf (PDF-specific). It explicitly mentions the output format ('clean, readable markdown format'), which helps differentiate its 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 context for when to use the tool ('when you need to analyze text content from websites, bypass paywalls, or get structured data'), including specific use cases like reading articles or documentation. However, it does not explicitly state when NOT to use it or name alternatives among sibling tools, such as extract_pdf for PDF files or parallel_read_url for batch 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?

No annotations are provided, so the description carries full burden. It mentions the tool is 'perfect for finding research papers' and lists use cases, but doesn't disclose behavioral traits like rate limits, authentication needs, pagination behavior, error handling, or what the return format looks like. For a search tool with no annotation coverage, this leaves significant gaps in understanding how it 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 appropriately sized with two sentences: first states the purpose, second provides usage guidelines. It's front-loaded with the core function and avoids unnecessary repetition. Every sentence adds value, though it could be slightly more concise by integrating the examples more tightly.

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 good purpose and usage context but lacks behavioral details (e.g., return format, error cases). For a search tool with 3 parameters and 100% schema coverage, it's adequate but has clear gaps in transparency that reduce completeness.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain query syntax further or provide examples of tbs values). 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 searches academic papers and preprints on arXiv repository, specifying the resource (arXiv repository) and verb (search). It distinguishes from siblings like search_web, search_images, and search_ssrn by focusing specifically on academic/scientific content, making the purpose specific and differentiated.

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: 'when researching scientific topics, looking for papers by specific authors, or finding the latest research in fields like AI, physics, mathematics, computer science, etc.' It doesn't explicitly state when NOT to use it or name alternatives (e.g., parallel_search_arxiv), but the context is sufficiently detailed for informed usage.

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 search scope ('across the web'), the default return format ('small base64-encoded JPEG images'), and the alternative return option ('return image URLs, title, shapes, and other metadata'). It doesn't mention rate limits, authentication needs, or pagination behavior, but covers the essential operational characteristics.

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 a distinct purpose: stating the tool's function, providing usage guidelines, and describing return behavior. It's front-loaded with the core purpose and avoids unnecessary repetition. The final sentence about default return format 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?

For a search tool with 6 parameters (1 required), 100% schema coverage, and no output schema, the description provides good contextual completeness. It covers the tool's purpose, usage scenarios, and key behavioral characteristics. The main gap is the lack of information about response format details beyond the base64/URL distinction, but given the schema coverage and tool complexity, this is reasonably 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 mentions the default return behavior (base64 images vs URLs) which relates to the 'return_url' parameter, but doesn't add significant semantic value beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Search for images across the web') and distinguishes it from siblings by specifying it's for visual content like photos, illustrations, diagrams, etc. It explicitly mentions it's 'similar to Google Images' which provides clear context about its 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 explicit guidance on when to use this tool: 'Use this when you need to find photos, illustrations, diagrams, charts, logos, or any visual content.' It gives three specific use cases: illustrating concepts, locating specific pictures, or discovering visual resources. This clearly distinguishes it from text-based search siblings like search_web or search_arxiv.

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. While it mentions what content is searched (Jina blog/news), it doesn't describe important behavioral traits: whether results are paginated, what format they return, authentication requirements, rate limits, or error conditions. For a search tool with no annotation coverage, 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 appropriately sized with two sentences that efficiently convey purpose and usage. The first sentence establishes what the tool searches and where, while the second provides use cases. There's minimal redundancy, though the second sentence could be slightly more concise by combining some elements.

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 transparency. The description covers what content is searched and use cases, but without annotations or output schema, it should ideally describe result format, pagination, or other behavioral aspects to be more 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 fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions general search terms but doesn't provide additional syntax, format, or usage details 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 tool searches Jina AI news and blog posts at a specific URL (jina.ai/news), listing specific content types (articles about AI, machine learning, neural search, embeddings, Jina products) and use cases (official documentation, tutorials, product announcements, technical deep-dives). It distinguishes from sibling tools like search_arxiv or search_web by specifying the Jina-specific source and content focus.

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

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

The description provides clear context for when to use this tool: to find Jina-specific content including documentation, tutorials, announcements, and technical articles. It doesn't explicitly state when NOT to use it or name specific alternatives, but the Jina focus implicitly distinguishes it from general web search siblings like search_web.

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 the platform (SSRN) and subject areas, it doesn't disclose behavioral traits like rate limits, authentication requirements, pagination behavior, error conditions, or what the return format looks like (since no output schema exists). The description adds some context about academic focus but lacks operational 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?

Two well-structured sentences that efficiently convey purpose and usage guidelines. The first sentence establishes core functionality, the second provides usage context. No wasted words, though it could be slightly more concise by combining some elements.

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 good purpose and usage context but lacks behavioral transparency about how results are returned, formatted, or limited. The absence of output schema means the description should ideally hint at return structure, which it doesn't.

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 descriptions. Baseline 3 is appropriate when the schema does the heavy lifting, though no additional value is added.

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 ('Search') and resource ('academic papers and preprints on SSRN'), and distinguishes from siblings by specifying the academic/social sciences focus versus general web search (search_web) or other academic sources (search_arxiv). It explicitly mentions the Social Science Research Network platform.

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

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

The description provides explicit when-to-use guidance: 'Perfect for finding research papers in social sciences, economics, law, finance, accounting, management, and humanities' and 'Use this when researching social science topics, looking for working papers, or finding the latest research in business and economics fields.' It implicitly distinguishes from parallel_search_ssrn by not mentioning parallel capabilities.

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 provides 'current information' and 'up-to-date information,' which implies freshness but doesn't disclose rate limits, authentication needs, or result format. The description adds some behavioral context (e.g., 'ideal for answering questions about recent events') but lacks details on pagination, error handling, or performance characteristics.

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. Every sentence earns its place by providing specific guidance (e.g., 'Ideal for answering questions about recent events') without redundancy or fluff.

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

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

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is moderately complete. It covers purpose and usage well but lacks details on behavioral traits like rate limits or result structure. Without annotations or output schema, more context on what the tool returns or its limitations would improve completeness.

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

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

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'query' syntax or 'tbs' usage). With high schema coverage, the baseline is 3, as the description doesn't compensate with additional param semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search the entire web for current information, news, articles, and websites.' It specifies the verb ('Search') and resource ('the entire web'), and distinguishes from siblings like search_arxiv, search_ssrn, and search_images by emphasizing web content rather than academic papers or 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: 'Use this when you need up-to-date information, want to find specific websites, research topics, or get the latest news.' It provides clear alternatives by naming specific use cases (e.g., 'answering questions about recent events') and implicitly distinguishes from siblings like search_arxiv for academic content.

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 a bearer token, which implies a read-only operation, but it doesn't disclose behavioral traits like security implications, rate limits, or whether the token is exposed in logs. The mention of 'debug' hints at a sensitive use case, but details are lacking.

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, clear sentence that states the purpose upfront. It's appropriately sized for a simple tool, though it could be slightly more polished (e.g., 'Returns' instead of 'Return'). Overall, it's efficient with minimal 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 complexity (a tool that returns sensitive authentication data) and no annotations or output schema, the description is incomplete. It doesn't explain the return format, security warnings, or potential side effects, which are crucial for such a tool. The 'debug' context is insufficient for safe usage.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info beyond the schema, but with zero params, the baseline is 4 as it adequately handles the absence of 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 states the tool 'Return[s] the bearer token from the Authorization header of the MCP settings' which specifies the verb ('Return') and resource ('bearer token'), but it's somewhat vague about the exact purpose beyond debugging. It doesn't clearly differentiate from sibling tools, which are unrelated to API key retrieval.

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

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

The description mentions 'used to debug', implying a context for usage, but it doesn't provide explicit guidance on when to use this tool versus alternatives or any exclusions. No sibling tools are directly related, so there's minimal comparative 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 carries the full burden. It mentions the tool uses the Jina Reranker API, which implies external service calls and potential rate limits or authentication needs, but does not explicitly disclose these behavioral traits. The description is accurate but lacks details on performance, errors, or output format.

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 first sentence stating the core functionality. Each subsequent sentence adds useful context without redundancy, making it efficient and well-structured with zero 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 (3 parameters, no annotations, no output schema), the description is reasonably complete. It explains what the tool does, when to use it, and the API involved, but could improve by detailing output format or error handling. Without an output schema, some gaps remain in understanding the return values.

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 clear descriptions for all three parameters. The description adds minimal value beyond the schema by implying the tool's purpose involves ranking documents against a query, but does not elaborate on parameter usage, constraints, or examples beyond what the schema already states.

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 ('rerank a list of documents by relevance') and resources ('documents'), using the Jina Reranker API. It distinguishes from sibling tools by focusing on document relevance ranking rather than search, extraction, or other operations listed among siblings.

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 ('when you have multiple documents and want to sort them by how well they match a specific query or topic') and gives examples of use cases ('document retrieval, content filtering, or finding the most relevant information from a collection'). However, it does not explicitly state when not to use it or name specific alternatives among siblings.

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