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 states the tool retrieves information, implying a read-only operation, but doesn't address potential authentication needs, rate limits, error conditions, or the format/scope of the returned information. This leaves significant gaps for a tool that likely interacts with external systems.

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 directly states the tool's purpose without any redundant or unnecessary information. It's appropriately sized and front-loaded, making it easy to parse 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, how it differs from sibling tools, or any behavioral constraints. For a tool that likely returns structured data about parsers, more context is needed to guide effective use.

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

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

The input schema has 100% description coverage, with the single parameter 'parser_id' clearly documented as a string identifier with examples. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.

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 'Get' and the resource 'detailed information about a specific parser', making the purpose unambiguous. However, it doesn't distinguish this tool from its sibling 'list_parsers', which might provide similar information in a different format or scope.

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

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

No guidance is provided on when to use this tool versus alternatives like 'list_parsers' or the various search tools. The description implies it's for detailed information on a specific parser, but it doesn't specify prerequisites, exclusions, or comparative use cases with 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 full burden. It states the tool checks health status but doesn't disclose behavioral traits like what the check entails (e.g., ping, metrics retrieval), response format, error handling, or rate limits. 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 a single, efficient sentence with no wasted words. It's front-loaded with the core purpose and appropriately sized for a simple tool. Every part of the sentence earns its place by conveying 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 tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior or output, which are important for a health check tool. Without annotations or output schema, more context on what 'health status' returns 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?

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline of 4 is applied for zero-parameter tools when the schema is fully covered.

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 'Check Redis API health status' clearly states the tool's purpose with a specific verb ('Check') and resource ('Redis API health status'). It distinguishes from sibling tools that are all search-related, making its purpose distinct. However, it doesn't specify what 'health status' entails (e.g., connectivity, metrics, uptime), keeping it from a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, frequency, or context for usage, such as troubleshooting or monitoring. With no explicit when/when-not statements or named alternatives, it offers minimal usage direction.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('List') but doesn't describe traits like whether it's read-only, paginated, rate-limited, or requires authentication. The mention of 'with details' hints at output richness but lacks specifics on format or constraints, leaving 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 a single, efficient sentence that front-loads the core purpose without any wasted words. It directly communicates the tool's function in a structured manner, making it easy for an agent to parse and understand quickly. Every part of the sentence contributes to 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 low complexity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It states what the tool does but lacks behavioral context and usage guidelines, which are important even for simple tools. Without annotations or output schema, more detail on expected behavior 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter information, as it's unnecessary for a parameterless tool. This aligns with the baseline expectation for such cases, providing no extraneous details.

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 ('List') and resource ('all available parsers'), making the purpose immediately understandable. It distinguishes from siblings like 'get_parser_info' by implying a comprehensive listing rather than specific details, though it doesn't explicitly contrast them. The description avoids tautology by specifying 'with details' beyond just the name.

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

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

No guidance is provided on when to use this tool versus alternatives like 'get_parser_info' or the various search tools. The description implies usage for obtaining a full list of parsers, but it doesn't specify contexts, prerequisites, or exclusions. This leaves the agent without explicit direction on 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions timeout behavior which is useful, but doesn't describe rate limits, authentication requirements, result format, pagination, or error conditions. For a web 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 extremely concise - just two sentences that directly state the tool's purpose and parameter information. Every word earns its place with zero wasted text, making it easy to parse 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 no annotations and no output schema, the description is insufficient. It doesn't explain what format results are returned in, what data fields are included, whether there are usage limits, or how to interpret search results. The timeout mention is helpful but doesn't compensate for the broader context gap.

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 by restating parameter names and the timeout default, but doesn't provide additional context beyond what's 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 performs 'Bing web search' with a specific verb and resource. It distinguishes itself from siblings like search_google_search by specifying the search engine, but doesn't explicitly contrast functionality differences beyond the engine name.

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

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

No guidance is provided about when to use this tool versus the many alternative search tools on the server (search_google_search, search_duckduckgo, etc.). The description only states what the tool does, not when it's the appropriate choice among 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'timeout' parameter which hints at potential delays, but doesn't describe what 'ChatGPT with web search' actually does behaviorally - whether it returns web results, generates responses based on web content, requires authentication, has rate limits, or what format results come in. For a search tool with zero annotation coverage, this is insufficient.

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

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

The description is extremely concise - just two sentences that get straight to the point. However, the second sentence about parameters feels redundant given the comprehensive schema. The structure is front-loaded with the core purpose first, which is good, but could be more efficient by eliminating the parameter restatement.

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

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

For a search tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'ChatGPT with web search' means operationally, what kind of results to expect, or how it differs from other search tools. Given the rich sibling context with many alternative search engines, this description leaves too many questions unanswered about when and why to use this specific 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 fully documents both parameters. The description adds minimal value by restating parameter names and types that are already in the schema. It does mention 'default 90' for timeout which is already in the schema's default field. Baseline 3 is appropriate when schema does all the 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 as 'ChatGPT with web search' which indicates it performs search functionality using ChatGPT. It distinguishes from siblings by specifying 'ChatGPT' rather than other search engines like Bing or Google. However, it doesn't explicitly contrast with other ChatGPT-related tools that might exist.

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

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

The description provides no guidance on when to use this tool versus the many sibling search tools (search_bing_search, search_google_search, search_claude, etc.). There's no indication of what makes ChatGPT search different or when it might be preferred over alternatives. The agent receives no usage context beyond the tool name.

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 a timeout parameter but doesn't explain what happens when timeout is reached, what type of response to expect (text, structured data, etc.), whether this makes API calls to external services, or any rate limits. For an AI assistant tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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

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

The description is extremely concise at just two sentences, with zero wasted words. It's front-loaded with the main purpose statement. However, the extreme brevity comes at the cost of completeness - more information would be helpful for this type of tool.

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 an AI assistant tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of response to expect, how results are formatted, whether this is a conversational interface or one-off query, or any authentication requirements. The agent needs more context to use this tool effectively compared to simpler tools.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds the parameter names and basic types but provides no additional semantic context beyond what's in the schema descriptions. The baseline 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 states 'Anthropic Claude assistant' which identifies the resource, but the verb 'search' is ambiguous - it could mean searching Claude's knowledge, using Claude as a search engine, or querying Claude as an AI assistant. The description doesn't clarify what specific action is performed with Claude, making the purpose somewhat vague. It distinguishes from non-Claude siblings but not clearly from other AI assistants in the list.

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

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

The description provides no guidance on when to use this tool versus the many alternatives in the sibling list (search_chatgpt, search_gemini, search_grok, etc.). There's no indication of Claude's specific capabilities, strengths, or appropriate use cases compared to other AI models or search engines. The agent receives no help in selecting between these similar tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions a timeout parameter but doesn't explain what happens during or after the search (e.g., response format, error handling, rate limits, or authentication needs). For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 brief and front-loaded with the tool's purpose, followed by parameter details. It avoids unnecessary words, but could be more structured (e.g., separating purpose from parameter explanations). Overall, it's efficient with little waste, though not perfectly organized.

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 search tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like response format, error cases, or how results are returned, which are critical for an AI agent to use the tool effectively. This leaves key contextual 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?

The description adds minimal value beyond the input schema, which has 100% coverage. It lists the parameters (query and timeout) and provides a default for timeout, but doesn't offer additional context like query formatting examples or timeout implications. Since the schema already documents these well, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose as 'Microsoft Copilot search' with the verb 'search' and resource 'Microsoft Copilot', making it understandable. However, it doesn't explicitly differentiate from sibling tools like search_bing_search or search_chatgpt, which are also search tools but for different services, so it doesn't fully distinguish from alternatives.

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

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

The description provides no guidance on when to use this tool versus alternatives, such as the sibling tools search_bing_search or search_chatgpt. It lacks context on specific use cases, prerequisites, or exclusions, leaving the agent without clear direction for selection among similar tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions a timeout parameter which hints at potential latency considerations, but doesn't describe what the tool actually does (is it conversational AI, search, or something else?), what kind of responses to expect, whether it requires authentication, rate limits, or any other behavioral characteristics. The description is minimal and leaves critical 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 extremely concise - just one sentence that lists parameters. While this is efficient, it may be too brief given the lack of other contextual information. The structure is straightforward but doesn't follow a typical pattern of stating purpose first then details. Every word serves a purpose, but the description might benefit from more complete 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 an AI assistant tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, what kind of interactions it supports, or how it differs from similar tools. For a tool that presumably generates AI responses, the description should provide more context about capabilities, limitations, and expected outputs.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters (query and timeout). The description adds no additional semantic information beyond what's in the schema - it simply repeats the parameter names and types. 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 states 'DeepSeek AI assistant' which indicates the tool interacts with DeepSeek, but it's vague about what specific action it performs. 'Search' in the name suggests querying, but the description doesn't explicitly state whether this is for information retrieval, conversation, or another purpose. It distinguishes from some siblings by mentioning DeepSeek specifically, but doesn't clearly differentiate from other AI assistant tools like search_chatgpt or search_claude.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple AI assistant/search tools available (search_chatgpt, search_claude, search_gemini, etc.), there's no indication of DeepSeek's specific strengths, use cases, or when it might be preferred over other options. The description only lists parameters without contextual usage information.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a timeout parameter with a default value, which hints at potential delays, but doesn't describe what the tool actually returns (search results format), error conditions, rate limits, or privacy implications (DuckDuckGo's focus). 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 extremely concise and front-loaded, with the core purpose stated first followed by brief parameter notes. Every sentence earns its place: the first establishes the tool's function, and the second efficiently lists parameters. There's zero waste 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 (search operation with timeout), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., search results format, links, snippets), error handling, or how it differs from sibling search tools. The description provides basic parameter info but misses critical 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%, with both parameters (query and timeout) fully documented in the schema. The description adds minimal value beyond the schema, merely restating parameter names and basic types. It doesn't provide additional context about query formatting, timeout implications, or search result limitations. 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: 'DuckDuckGo search' specifies both the verb (search) and resource (DuckDuckGo). It distinguishes from siblings by naming the specific search engine, though it doesn't explicitly contrast with other search tools like search_bing_search or search_google_search. The purpose is unambiguous but lacks explicit sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling search tools (e.g., search_bing_search, search_google_search, search_chatgpt), there's no indication of DuckDuckGo's specific use cases, advantages, or limitations compared to others. The description only lists parameters without contextual usage information.

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 timeout behavior which is useful, but doesn't describe what the tool actually does (generates text? answers questions? searches web?), what format the response takes, rate limits, authentication requirements, or error conditions. For an AI 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 brief but inefficiently structured. The first part 'Google Gemini AI' is vague, and the 'Args' section duplicates schema information. While concise, it doesn't front-load the most important information about what the tool actually does. The two-sentence format could be more effectively organized.

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/AI tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of results to expect, how Gemini differs from other AI services, or what the tool actually produces. Given the complexity of AI tools and the rich sibling tool ecosystem, this description leaves too many contextual 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 both parameters. The description adds minimal value by restating parameter names and types in a less structured format. It doesn't provide additional semantic context beyond what's in the schema descriptions, so 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 as 'Google Gemini AI' which identifies the specific AI service being accessed. However, it doesn't distinguish this from sibling tools like search_chatgpt or search_claude beyond naming the service. The 'Args' section adds operational context but doesn't fully articulate the action being performed beyond 'search'.

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

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

The description provides no guidance about when to use this tool versus the many sibling search tools (search_bing_search, search_chatgpt, search_claude, etc.). There's no indication of Gemini's specific capabilities, strengths, or appropriate use cases compared to alternatives. The agent receives no help in tool selection decisions.

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 a timeout parameter but doesn't describe other key traits like rate limits, authentication needs, result format, pagination, or error handling. For a web search tool, this leaves significant gaps in understanding its behavior.

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

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

The description is extremely concise and front-loaded, stating the purpose in the first three words. The parameter information is brief and directly relevant, with no wasted words, making it efficient 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 complexity of a web search tool with no annotations and no output schema, the description is incomplete. It lacks details on return values, error cases, performance expectations, and how it differs from sibling tools, leaving the agent under-informed 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?

The schema description coverage is 100%, so the schema already documents both parameters fully. The description adds minimal value by restating the parameters and their types, but doesn't provide additional context like query formatting tips or timeout implications beyond what's in the schema.

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

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

The description clearly states the tool's purpose as 'Google web search' with a specific verb ('search') and resource ('Google web'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'search_bing_search' or 'search_duckduckgo' beyond the Google branding, missing explicit distinction.

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

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

No guidance is provided on when to use this tool versus alternatives like the many other search siblings (e.g., Bing, DuckDuckGo, ChatGPT). The description lacks context on use cases, exclusions, or comparisons, leaving the agent to infer usage based on tool names 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions a timeout parameter but doesn't explain what happens when timeout is reached, whether this is a read-only or mutating operation, what authentication might be required, or what the typical response format looks like. For a search/assistant tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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

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

The description is very brief and to the point - just two short sentences. While it could be more informative, there's no wasted verbiage. The structure is simple but effective for its limited content.

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 search/assistant tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of results to expect, whether this is conversational or single-response, what the Grok assistant specializes in, or how it differs from other AI assistants in the sibling tools. The description leaves too many contextual 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 both parameters. The description adds minimal value by restating parameter names and types that are already clear in the schema. It doesn't provide additional context about query formatting expectations or timeout implications beyond what the schema descriptions state.

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 'xAI Grok assistant' which identifies the resource, but the verb 'search' is only implied by the tool name. It doesn't clearly distinguish this from sibling tools like search_chatgpt or search_claude, nor does it specify what kind of search/assistance this provides. The purpose is somewhat vague rather than specific.

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

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

No guidance is provided about when to use this tool versus the many sibling search/assistant tools. The description doesn't mention any specific context, prerequisites, or alternatives. The agent must infer usage from the tool name alone, which provides minimal direction.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'AI-powered search with sources' which gives some context about the nature of results, but doesn't describe rate limits, authentication requirements, response format, pagination, error conditions, or what 'sources' actually means. 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 extremely concise at just two sentences. The first sentence states the core purpose, and the second lists parameters. While efficient, it might be too brief given the lack of usage guidance and behavioral context that would help an agent use this tool effectively among many alternatives.

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 search tool with 12 sibling alternatives, no annotations, and no output schema, the description is incomplete. It doesn't help an agent understand when to choose this tool over others, what the response format looks like, or any behavioral constraints. The agent would need to guess or trial-and-error to use this tool 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 fully documents both parameters. The description briefly mentions 'Args: query (string), timeout (int, default 90)' which adds no meaningful semantic information beyond what's in the schema. 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 as 'AI-powered search with sources', which specifies both the action (search) and key characteristics (AI-powered, includes sources). It distinguishes itself from generic search tools by highlighting the AI-powered aspect, though it doesn't explicitly differentiate from sibling AI search tools like search_chatgpt or search_gemini.

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

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

The description provides no guidance on when to use this tool versus the 12 sibling search tools on the server. It doesn't mention any specific use cases, advantages over alternatives, or scenarios where other tools might be more appropriate. The agent must infer usage from the tool name 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 of behavioral disclosure. While it mentions a timeout parameter, it doesn't describe what the tool returns (e.g., video results, metadata), whether it's a read-only operation, potential rate limits, authentication needs, or error conditions. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

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

The description is extremely concise and front-loaded, stating the core purpose in the first three words. The additional parameter information is brief and directly relevant, with zero wasted words or redundant explanations.

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 lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't explain what the search returns (e.g., video links, titles, durations), how results are structured, or any limitations (e.g., result count, sorting). For a search tool with no structured output documentation, this creates uncertainty for 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?

The description adds minimal value beyond the input schema, which already has 100% coverage with clear descriptions for both parameters. It briefly mentions the parameters but doesn't provide additional context about query formatting, timeout implications, or YouTube-specific search syntax. The baseline of 3 is appropriate since 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 a specific verb ('Search') and resource ('YouTube videos'), making it immediately understandable. However, it doesn't explicitly distinguish this tool from its sibling search tools (like search_google_search or search_bing_search), which would require mentioning YouTube-specific search capabilities or results.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling search tools available (e.g., search_google_search, search_bing_search), there's no indication of when YouTube search is preferred, what makes it unique, or any prerequisites for use.

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