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

With no annotations provided, the description carries full burden and delivers comprehensive behavioral disclosure. It details platform auto-detection logic, intent detection algorithms, keyword priority rules, query structuring tips, and exactly what the tool returns (including _meta data, dark mode handling, and platform-specific guidelines). This goes far beyond basic functionality description.

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

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

The description is well-structured with clear sections (WHEN TO USE, PLATFORM DETECTION, etc.), but at ~250 words it's quite lengthy. While most content is valuable, some details like specific keyword lists and detection algorithms might be excessive for a tool description. The information is front-loaded with the core purpose, but could be 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 complex 7-parameter tool with no annotations and no output schema, the description provides exceptional completeness. It covers behavioral logic, parameter interactions, usage scenarios, return format details, and practical examples. The agent has everything needed to understand when and how to use this tool effectively despite the absence of structured metadata.

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?

While schema description coverage is 100%, the description adds significant value by explaining the interaction between parameters (e.g., platform parameter overrides auto-detection, mode='dark' triggers dark-optimized palettes) and providing practical examples of how query parameter structure affects intent detection. It also offers query formatting recommendations not present 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 with a specific verb ('generate') and resource ('complete design system') while distinguishing it from sibling tools that search for components, patterns, platforms, etc. rather than generating integrated systems. The opening sentence precisely defines what the tool creates.

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 includes an explicit 'WHEN TO USE' section that lists three specific scenarios (starting new project, needing cohesive foundation, wanting all elements in one call). It also distinguishes this tool from sibling search tools by emphasizing it provides 'all design elements in one call' rather than searching for individual components.

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 so effectively. It discloses key behavioral traits: intelligent auto-detection of domains, platform keyword detection with cross-platform code equivalents, natural language query processing, and result grouping by domain with relevance scores. It doesn't mention rate limits or authentication needs, but provides substantial operational 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 well-structured with clear sections (WHEN TO USE, PLATFORM DETECTION, QUERY TIPS, RETURNS, EXAMPLES) and front-loaded with the core purpose. While comprehensive, some sections could be more concise, and every sentence appears to earn its place by providing distinct value.

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 substantial context: purpose, usage guidelines, behavioral traits, query examples, and return format details. It covers the complexity of a multi-domain search tool well, though doesn't explicitly mention error conditions or pagination behavior for the results.

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 doesn't add specific parameter details beyond what's in the schema (query and max_results), though it provides query tips ('Use natural language describing your design goal') that offer semantic guidance for the query parameter without contradicting 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 explicitly states the tool performs 'unified search across ALL design domains' and lists 10 specific resource types (styles, colors, typography, etc.), providing a specific verb+resource combination. It clearly distinguishes from siblings by being the 'DEFAULT tool for broad queries' versus domain-specific tools like search_components or search_styles.

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 includes a dedicated 'WHEN TO USE' section that explicitly states: 'DEFAULT tool for broad queries. Use when unsure which specific tool, or need multi-domain results.' This provides clear guidance on when to use this tool versus its domain-specific siblings, with explicit alternatives implied.

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 what the tool returns (icon import codes with JSX, chart recommendations with library suggestions) and includes examples, but lacks details on permissions, rate limits, or error handling. It adds some behavioral context but not comprehensively.

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

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

The description is well-structured with clear sections (description, when to use, type filter, returns, examples), front-loaded with key information, and every sentence adds value without redundancy. It's appropriately sized for the tool's complexity.

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 a good job explaining the tool's purpose, usage, and returns. It covers the essential context for a search tool with examples, but could improve by detailing output format or error cases more explicitly. It's mostly complete for its complexity.

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 well. The description adds minimal value by mentioning the 'TYPE FILTER' and 'RETURNS' sections, but doesn't provide additional syntax or format details beyond what the schema offers. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool searches for UI components, specifically Lucide icons (176+) and chart types (37+), distinguishing it from siblings like search_patterns or search_styles by focusing on icons and charts for data visualization. It uses specific verbs ('search') and resources ('UI components') with explicit 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?

The description includes an explicit 'WHEN TO USE' section that states 'Finding icons for UI, selecting chart types for data,' providing clear guidance on when to use this tool. It differentiates from siblings by focusing on icons and charts, though it doesn't name alternatives explicitly, the context is sufficient.

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 what the tool returns (section structures, do/don't practices, conversion tips, code examples) and provides examples of search queries. However, it doesn't mention potential limitations like rate limits, authentication requirements, or pagination behavior, which would be helpful for a search tool.

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

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

The description is well-structured with clear sections (purpose, when to use, type filter, returns, examples) and every sentence adds value. It's appropriately sized for the tool's complexity, with no redundant information, and is front-loaded with the core purpose.

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 output schema, the description does a good job explaining what the tool returns and providing examples. However, without annotations and with no output schema, it could benefit from more detail about the return format structure or error conditions. The combination of purpose, usage guidelines, and return description makes it mostly complete for this context.

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

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

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description adds minimal value beyond the schema by mentioning the TYPE FILTER options and providing example queries, but doesn't explain parameter interactions or provide additional semantic context that isn't already in the schema descriptions.

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

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

The description clearly states the tool searches design patterns with specific categories (landing page layouts, UX guidelines, product type recommendations) and includes quantitative counts (60+, 130+, 117+). It distinguishes from siblings like search_components or search_platforms by focusing specifically on design patterns rather than components, platforms, or other search 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 explicitly includes a 'WHEN TO USE' section listing specific contexts: page layouts, UX best practices, accessibility, and navigation patterns. This provides clear guidance on when this tool is appropriate versus alternatives, helping the agent choose between this and sibling tools like search_components or search_all.

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 what the tool returns (platform-specific patterns with cross-platform implementation code) and provides examples of search queries. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions.

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

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

The description is well-structured with clear sections (description, when to use, returns, parameters, examples) and uses bullet points effectively. While comprehensive, it could be slightly more concise by integrating some sections 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?

For a search tool with 3 parameters and no output schema, the description provides good context about what it searches, when to use it, what it returns, and includes helpful examples. The main gap is the lack of output format details, but the description compensates well with the 'RETURNS' section explaining the content structure.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters but doesn't add meaningful semantic context beyond what's in the schema. The baseline 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 tool searches iOS Human Interface Guidelines or Android Material 3 design patterns, specifying both the resources (platform guidelines) and the scope (design patterns). It distinguishes from siblings by focusing specifically on platform-specific patterns rather than general design systems, components, or styles.

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 includes an explicit 'WHEN TO USE' section that lists specific scenarios: iOS design patterns, SwiftUI style in Flutter, Material 3 patterns in React Native, and platform-specific UI/UX. This provides clear guidance on when to use this tool versus its siblings like search_components or search_styles.

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 detailing what the tool returns (Category, Guideline, Description, Do/Don't, Code Examples, Severity, Docs URL) and providing query examples. It doesn't mention rate limits, authentication needs, or pagination behavior, but covers the core functionality comprehensively.

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 with clear sections (purpose, when to use, query tips, returns, examples), each earning its place. It's front-loaded with the core purpose, uses bullet-like formatting for readability, and contains zero wasted 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?

For a search tool with no output schema and no annotations, the description does an excellent job covering purpose, usage, parameters, and return format. The only minor gap is lack of explicit mention of error cases or what happens when no results are found, but otherwise it's highly complete given the context.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters well. The description adds value through 'QUERY TIPS' that clarifies how to structure the query parameter and 'EXAMPLES' that show parameter usage, but doesn't provide additional semantic context beyond what's in the schema. 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 searches for framework-specific guidelines, listing all 12 supported frameworks explicitly. It distinguishes from siblings by specifying 'framework-specific guidelines' rather than general patterns, components, or platforms covered by other tools like search_patterns or search_components.

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 includes a dedicated 'WHEN TO USE' section that explicitly states 'Framework-specific patterns, component best practices, state management.' It also provides 'QUERY TIPS' instructing to include framework name AND topic, giving clear guidance on when and how to use this tool versus 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 and does well by specifying what the tool returns (Style CSS code, color hex values with Tailwind config, font pairings with Google Fonts imports) and providing examples. It doesn't mention rate limits, authentication needs, or destructive behavior, but covers core functionality adequately.

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

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

The description is well-structured with clear sections (purpose, usage, domain filter, returns, examples), front-loaded with key information, and every sentence adds value without redundancy. It efficiently communicates necessary details in a compact format.

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

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

Given no annotations and no output schema, the description does a good job covering purpose, usage, parameters, and return values. It could improve by mentioning potential limitations or error cases, but it's largely complete for a search tool with moderate complexity.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the optional 'domain' parameter with its filter options (style, color, typography, prompt) and clarifying what the tool searches for, enhancing understanding beyond the schema's technical definitions.

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

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

The description explicitly states the tool searches for specific visual design elements (UI styles, color palettes, typography pairings, AI prompts) with concrete counts, making the purpose clear and specific. It distinguishes from siblings by focusing on visual aesthetics rather than broader design systems, components, or platforms.

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 includes a dedicated 'WHEN TO USE' section listing specific scenarios (visual aesthetics, color schemes, font choices, CSS effects, design tokens), providing explicit guidance on when to use this tool. It implicitly differentiates from siblings by not covering their domains like components or platforms.

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