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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool returns text snippets (not files), and post-call actions are required (editing/adding files). However, it doesn't mention authentication needs, rate limits, error conditions, or what happens if parameters are invalid. For a 5-parameter tool with no annotations, this leaves significant behavioral gaps.

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

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

The description is appropriately sized with three sentences that each serve distinct purposes: when to use, what the tool does, and post-call instructions. It's front-loaded with the primary usage scenario. While efficient, the third sentence about post-call actions could be slightly more integrated with the tool's 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?

Given 5 parameters with 100% schema coverage but no annotations and no output schema, the description provides adequate context about when and how to use the tool. However, it doesn't explain what the return value looks like (only says 'text snippet' without format details) or address potential complexities like error handling. For a tool that generates code snippets, more output information 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 5 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description, which applies here.

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 'returns the text snippet for that UI component' when users request new UI components, providing a specific verb (returns) and resource (text snippet for UI component). It distinguishes from siblings by specifying this tool only returns snippets, while siblings likely handle inspiration or refinement. However, it doesn't explicitly name the sibling tools for comparison.

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

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

The description provides explicit usage guidance: 'Use this tool when the user requests a new UI component' with concrete examples (mentions /ui, /21, /21st, or asks for specific components like button, input, etc.). It also specifies 'This tool ONLY returns the text snippet' and instructs what to do after calling ('you must edit or add files to integrate'), creating clear boundaries for when to use this tool versus other actions.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns JSON data and text snippets, and it adds context about post-call actions: 'After calling this tool, you must edit or add files to integrate the snippet into the codebase.' This gives useful behavioral insight into workflow integration. However, it lacks details on permissions, rate limits, or error handling, which are gaps for a tool with no annotations.

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

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

The description is appropriately sized and front-loaded, starting with usage context. Every sentence adds value: the first states when to use it, the second specifies the return type and limitation, and the third adds post-call instructions. There's minimal waste, though it could be slightly more structured for 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 no annotations, no output schema, and 2 parameters with full schema coverage, the description is moderately complete. It covers purpose, usage, and post-call actions, but lacks details on return format (beyond 'JSON data' and 'text snippet'), error cases, or integration specifics. For a tool with siblings and no structured output, it should do more to compensate for these gaps.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('message' and 'searchQuery'). The description doesn't add any meaning beyond what the schema provides—it doesn't explain how parameters interact or their practical use. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.

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 'returns the JSON data of matching components' and 'returns the text snippet for that UI component', which clarifies it fetches and returns component data. However, it doesn't clearly distinguish from sibling tools like '21st_magic_component_builder' or '21st_magic_component_refiner'—it mentions 'without generating new code' but doesn't specify how this differs from those siblings in purpose.

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 the user wants to see component, get inspiration, or /21st fetch data and previews from 21st.dev'. It also implies an alternative by stating 'without generating new code', which hints at sibling tools that might generate code. However, it doesn't explicitly name alternatives or specify when not to use it, such as for editing or refining 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?

No annotations are provided, so the description carries the full burden. It states the tool 'returns redesigned version of the component and instructions on how to implement it,' which gives some behavioral insight into outputs. However, it lacks details on permissions, side effects, error handling, or performance considerations. For a tool that modifies UI components, this is a significant gap in transparency.

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

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

The description is concise and front-loaded, with two sentences that directly address usage and purpose. There is no wasted text, and it efficiently communicates key information. However, the first sentence is slightly long and could be split for better readability, preventing a perfect score.

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

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

Given no annotations and no output schema, the description provides adequate context for usage and purpose but lacks details on behavioral aspects like error handling or output format. It compensates somewhat with clear guidelines, but for a tool that modifies UI components, more completeness on what 'redesigned version' entails would be beneficial. It meets minimum viability with clear gaps.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description does not add any parameter-specific information beyond what's in the schema (e.g., it doesn't clarify how 'context' should be derived or examples of 'userMessage'). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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: 'improves UI of components and returns redesigned version of the component and instructions on how to implement it.' It specifies the action (improve/refine/redesign) and resource (UI components), though it doesn't explicitly differentiate from sibling tools like '21st_magic_component_builder' or '21st_magic_component_inspiration' beyond scope limitations.

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

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

The description provides explicit usage guidelines: 'Use this tool when the user requests to re-design/refine/improve current UI component with /ui or /21 commands, or when context is about improving, or refining UI for a React component or molecule (NOT for big pages).' It specifies when to use (UI refinement requests, React components/molecules) and when not to use (big pages), offering clear context and exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure and does so effectively. It describes key behaviors: supporting single/multiple searches with category filtering, returning logos in different themes if available, and detailing what each result includes (component name, code, import instructions). However, it doesn't mention potential limitations like rate limits, authentication needs, or what happens when logos aren't found.

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 guidelines, examples, format options, result details) and front-loaded with the core functionality. While comprehensive, some sentences could be more concise (e.g., the example queries section is somewhat repetitive). Overall, it's efficiently organized with minimal wasted text.

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 substantial context: purpose, usage scenarios, examples, format details, and result structure. It adequately covers the tool's functionality given its complexity. The main gap is the lack of output schema, but the description compensates by detailing what results include, making it mostly complete for agent understanding.

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 value by explaining format options (TSX, JSX, SVG) and providing example queries that illustrate how to use the 'queries' parameter, but doesn't add significant semantic information beyond what's in the structured schema. This meets the baseline expectation 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 ('search and return logos') and resources ('logos in specified format'), and distinguishes it from sibling tools by focusing on logo search functionality rather than component building, inspiration, or refinement. The opening sentence provides a complete, unambiguous statement of what the tool does.

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 with a dedicated 'When to use this tool' section, listing two specific scenarios: when users type '/logo' commands and when they request logos not in the local project. It also includes five concrete example queries that demonstrate both command-style and request-style usage patterns, giving clear context for application.

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