MCP Tailwind Gemini Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but some overlap exists. For example, 'analyze_design' and 'suggest_improvements' both involve AI-powered design feedback, which could cause confusion. However, other tools like 'convert_to_tailwind' and 'optimize_classes' are clearly differentiated.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, such as 'analyze_design', 'create_layout', and 'generate_component'. This uniformity makes the tool set predictable and easy to understand.
Tool Count5/5
With 10 tools, the count is well-scoped for a Tailwind CSS and design assistance server. Each tool appears to serve a specific function in the domain, from project setup to component generation and optimization.
Completeness4/5
The tool set covers key aspects of Tailwind CSS workflows, including conversion, layout creation, theme generation, and component handling. A minor gap is the lack of tools for updating or deleting generated components or themes, but agents can likely work around this.
Average 2.9/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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{
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  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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 'AI analysis' but doesn't explain what that entails—whether it's a simple check, a detailed report, or something else. There's no information about execution time, rate limits, authentication requirements, or what happens to the input data. For a tool with 6 parameters and 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point without unnecessary words. It's appropriately sized for a tool with this complexity, though it could be more front-loaded with key details. There's no wasted language, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 annotations, no output schema), the description is incomplete. It doesn't explain what the analysis outputs, how results are formatted, or what 'improvements and best practices' entail. Without annotations or output schema, the agent lacks crucial information about the tool's behavior and results, making this description inadequate for confident tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all parameters well-documented in the schema itself. The description adds no additional parameter semantics beyond what's already in the schema—it doesn't explain relationships between parameters, provide examples, or clarify how 'context' interacts with analysis. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'analyze design with AI for improvements and best practices', which provides a general purpose but lacks specificity about what 'design' means or what types of improvements are offered. It doesn't clearly distinguish from sibling tools like 'suggest_improvements' or 'optimize_classes', leaving ambiguity about when to use each. The verb 'analyze' is clear, but the resource 'design' is vague without further context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'suggest_improvements' or 'optimize_classes'. There's no mention of prerequisites, specific scenarios where this analysis is appropriate, or any exclusions. The agent must infer usage from the tool name and parameters alone, which is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the conversion action but doesn't describe what happens during conversion (e.g., whether it's a read-only transformation or modifies data, error handling for invalid input, or performance considerations like rate limits). This leaves significant gaps for a tool that performs code transformation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose, making it easy to understand at a glance without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a code conversion tool with 4 parameters and no annotations or output schema, the description is incomplete. It doesn't explain the return values, error cases, or behavioral traits like whether the conversion is idempotent or has side effects. This makes it inadequate for guiding an AI agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond the schema, such as examples of input code or details on how 'preserveCustom' or 'optimize' affect the output. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and the resources 'CSS/SCSS to Tailwind classes', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'optimize_classes' or 'suggest_improvements', which might have overlapping functionality with CSS optimization or improvement suggestions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'optimize_classes' or 'suggest_improvements'. It lacks context about prerequisites, such as needing valid CSS/SCSS/HTML input, and doesn't mention any exclusions or specific scenarios where this tool is preferred over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'Generate' but doesn't specify whether this creates new files, modifies existing ones, requires authentication, has rate limits, or what the output format is (e.g., HTML code, a preview URL). For a tool with no 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Generate responsive layouts with Tailwind CSS'. It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a tool with a clear scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'Generate' entails (e.g., returns code, creates files), how layouts are delivered, or any behavioral traits like side effects. For a tool with 4 parameters and potential complexity in output, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 4 parameters (type, sections, complexity, framework) with enums and defaults. The description adds no parameter-specific information beyond implying Tailwind CSS usage, which is already suggested by the tool name. Baseline 3 is appropriate when the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate responsive layouts with Tailwind CSS'. It specifies the action ('Generate'), resource ('responsive layouts'), and technology ('Tailwind CSS'). However, it doesn't explicitly differentiate from sibling tools like 'create_project' or 'generate_component', which might also involve layout generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 siblings like 'create_project', 'generate_component', and 'suggest_improvements', there's no indication of context, prerequisites, or exclusions. The agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'AI assistance using Gemini,' hinting at external API usage, but lacks details on rate limits, authentication needs, output format (e.g., code snippets), error handling, or whether the generation is deterministic. For a tool with 9 parameters and no annotations, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Generate Tailwind CSS components with AI assistance using Gemini.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by specifying key elements.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, AI-driven generation, no output schema, and no annotations), the description is incomplete. It lacks information on output format (e.g., returns code as a string), behavioral traits (e.g., rate limits, Gemini integration details), and usage context relative to siblings. Without annotations or an output schema, the description should provide more context to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are documented in the schema with clear descriptions and enums. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain interactions between parameters like 'useShadcn' and 'framework'). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate Tailwind CSS components with AI assistance using Gemini.' It specifies the verb ('Generate'), resource ('Tailwind CSS components'), and method ('with AI assistance using Gemini'), which is specific and actionable. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_shadcn_component' or 'create_layout', which might also involve component generation or creation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like 'get_shadcn_component' (which might retrieve existing components) or 'create_layout' (which might focus on broader layouts), nor does it specify prerequisites, ideal use cases, or exclusions. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Generate visual preview' implies a read-only operation that creates output, it doesn't disclose important behavioral aspects like whether this is a computationally intensive operation, whether there are rate limits, what format the preview output takes (image, HTML, etc.), or whether authentication is required.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 6 words, front-loading the essential information with zero wasted words. Every word earns its place by specifying what's being generated (visual preview) and what it applies to (Tailwind components).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters and no output schema, the description is inadequate. It doesn't explain what the output looks like (image URL, base64 data, HTML snippet), doesn't mention performance characteristics, and provides no examples of typical use cases. With no annotations to supplement the description, this leaves significant gaps for an agent trying to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already documents all 5 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain how the HTML should be structured, what 'responsive breakpoint previews' actually means, or provide examples of typical usage patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Generate') and resource ('visual preview of Tailwind components'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'analyze_design' or 'generate_component' that might also involve visual aspects of Tailwind components.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like 'analyze_design', 'generate_component', and 'suggest_improvements' that might overlap in visual component contexts, there's no indication of when this specific preview generation tool is appropriate versus those other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 but offers minimal information. It mentions 'optimize and clean up' which implies transformation, but doesn't specify whether this is a read-only analysis or a destructive modification, nor does it cover aspects like performance impact, error handling, or output format. This leaves significant gaps for a tool that likely modifies input.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single, clear sentence that front-loads the core purpose without any wasted words. Every part of the description earns its place by directly stating what the tool does, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving optimization of CSS classes) and the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what 'optimize' entails in practice, what the output looks like, or any behavioral traits like safety or limitations, leaving the agent with inadequate context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with all parameters well-documented in the schema itself. The description adds no additional parameter semantics beyond what the schema provides, such as examples or edge cases. This meets the baseline score since the schema handles the heavy lifting, but the description doesn't compensate or enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('optimize and clean up') and target resource ('Tailwind CSS classes'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'suggest_improvements' or 'analyze_design', which might have overlapping functionality, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. Given sibling tools like 'suggest_improvements' and 'analyze_design' that might offer similar or complementary functions, the lack of explicit usage context or exclusions leaves the agent without clear direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'AI-powered suggestions' but doesn't explain how the tool behaves—e.g., whether it's read-only, requires specific permissions, has rate limits, or what the output format might be. This is a significant gap for a tool that likely involves analysis and recommendations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
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 unnecessary words. It's front-loaded and every part earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool that analyzes HTML for design improvements, the description is incomplete. With no annotations and no output schema, it fails to cover behavioral aspects like safety, permissions, or return values. The schema handles parameters well, but the overall context for agent decision-making is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 all parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify how 'context' or 'targetAudience' influence suggestions). 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get AI-powered suggestions') and resource ('design improvements'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'analyze_design' or 'optimize_classes', which might offer similar analysis functions, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'analyze_design' or 'optimize_classes'. It lacks context about prerequisites, ideal scenarios, or exclusions, leaving the agent to infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool 'Generate[s]' with 'AI assistance', implying a creation/mutation operation, but doesn't specify output format (e.g., JSON, CSS file), whether it's idempotent, or any rate limits or authentication needs. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Generate custom Tailwind theme with AI assistance'). It wastes no words and is appropriately sized for the tool's complexity, making it easy for an agent to parse quickly without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is minimally adequate. It clarifies the purpose but lacks usage guidelines and behavioral details. Without an output schema, it doesn't explain return values (e.g., theme object or config file), leaving the agent to infer results. This makes it incomplete for optimal tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, providing clear details for all 6 parameters (e.g., 'brandColor' as primary color, 'style' as design style with enum). The description adds no parameter-specific information beyond what's in the schema, such as explaining how 'AI assistance' influences parameter usage. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate') and resource ('custom Tailwind theme'), specifying it's done 'with AI assistance'. It distinguishes from siblings like 'analyze_design' or 'convert_to_tailwind' by focusing on theme creation rather than analysis, conversion, or other design tasks. However, it doesn't explicitly differentiate from 'create_layout' or 'create_project', which might also involve creation aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, such as needing a brand color, or compare it to siblings like 'convert_to_tailwind' for existing themes or 'suggest_improvements' for modifications. Without this context, the agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool retrieves source code and examples, implying a read-only operation, but does not specify details like authentication requirements, rate limits, error handling, or output format. For a tool with no 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Get shadcn/ui component source code and usage examples.' It is front-loaded with the core purpose, has no redundant information, and every word contributes to understanding the tool's function. This is an excellent example of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output specifics. The high schema coverage helps, but without annotations or output schema, more completeness would be beneficial for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all three parameters. The description adds no additional semantic information beyond what the schema provides, such as examples of component names beyond 'button' or 'card', or clarification on what 'usage examples' entail. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 shadcn/ui component source code and usage examples.' It specifies the verb ('Get'), resource ('shadcn/ui component'), and what is retrieved ('source code and usage examples'). However, it does not explicitly differentiate from sibling tools like 'generate_component' or 'create_project', which might have overlapping functionality in a UI component context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 does not mention any prerequisites, context for usage, or exclusions. Given sibling tools like 'generate_component' or 'analyze_design', the lack of differentiation leaves the agent without clear 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose whether this creates files locally, requires internet access, has side effects, handles errors, or provides progress feedback. For a project creation tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that immediately conveys the core functionality. Every word earns its place by specifying the exact technology stack, making it front-loaded and waste-free.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a project creation tool with 5 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what 'complete project' means in practice, what files are generated, whether dependencies are installed, or what happens after creation. The context signals indicate significant complexity that isn't addressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 doesn't add any parameter-specific context beyond implying the tool combines these technologies. 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create a complete project') and specifies the exact technologies involved ('Vite + Tailwind + shadcn/ui setup'). It distinguishes this from sibling tools like 'create_layout' or 'create_theme' by focusing on full project scaffolding rather than partial components.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'create_layout' or 'generate_component'. It doesn't mention prerequisites, dependencies, or scenarios where this tool is preferred over manual setup or other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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