UI/UX MCP Server

by willem4130

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have clearly distinct purposes targeting different aspects of UI/UX development (animation, component work, testing, styling, workflows). However, there is some potential overlap between 'component_analyze' and 'workflow_optimize_ux' as both involve analysis/optimization, and 'playwright_test_ui' and 'storybook_test_component' both involve testing, though their specific scopes (UI vs component) help differentiate them.
Naming Consistency4/5
The naming follows a consistent pattern of 'domain_action' or 'domain_verb_noun' (e.g., animation_create_timeline, component_analyze, storybook_get_stories). All tools use snake_case consistently. Minor deviations exist, such as 'playwright_capture_screenshots' using a verb_noun structure while others like 'tailwind_generate_config' use verb_noun, but overall it's highly readable and predictable.
Tool Count5/5
With 12 tools, the count is well-scoped for a UI/UX server covering animation, component development, testing, styling, and workflows. Each tool appears to serve a distinct function within this domain, and the number is neither too sparse nor overwhelming, fitting typical expectations for a specialized server.
Completeness4/5
The tool set covers key areas of UI/UX development comprehensively, including creation, analysis, testing, and optimization. Minor gaps exist, such as no explicit tools for updating or deleting components or animations, and limited coverage of collaboration features (e.g., version control or team workflows), but agents can likely work around these with the provided tools.
Average 2.7/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions analyzing for performance and accessibility but doesn't describe how the analysis is performed, what the output looks like, any side effects, rate limits, or authentication needs. This leaves significant gaps for an agent to understand the tool's behavior.
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 concise with a single sentence that directly states the tool's function. It is front-loaded and wastes no words, though it could benefit from more detail to improve clarity and completeness.
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 analysis tools, lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover return values, error handling, or detailed usage scenarios, making it inadequate for an agent to fully understand the tool's context and application.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It doesn't explain the 'code' parameter (e.g., what format or language) or the 'checks' parameter (e.g., what each enum value means or how they affect analysis). This fails to add meaningful semantics beyond the basic schema.
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 analyzes components for performance and accessibility, which provides a general purpose but lacks specificity about what 'analyze' entails (e.g., static analysis, runtime profiling, or compliance checking). It distinguishes from siblings like 'component_create' but not clearly from other analysis tools like 'playwright_test_ui' or 'storybook_test_component'.
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?
No explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for analyzing components, but it doesn't specify prerequisites, contexts, or exclusions, such as when to choose this over 'playwright_test_ui' for UI testing or 'storybook_test_component' for component testing.
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 'comprehensive UX optimization analysis' but doesn't clarify what this entails operationally—such as whether it's a read-only analysis, if it modifies anything, runtime characteristics, or output format. This leaves critical behavioral traits unspecified for a tool with potential complexity.
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 with no wasted words, making it appropriately concise. However, it's not front-loaded with critical details like parameter context or usage guidelines, which could improve structure. Overall, it's brief but lacks depth.
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 UX analysis with multiple parameter options), lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't address what the tool returns, how analyses are performed, or prerequisites, leaving the agent with insufficient information to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It adds no meaning beyond the schema—failing to explain what 'url' should point to (e.g., a webpage, design file) or what 'analyses' array does (e.g., selecting specific UX aspects to evaluate). With two parameters and no schema descriptions, this is a significant gap.
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 action ('Run comprehensive UX optimization analysis') which is a clear verb+resource combination, but it's vague about what 'comprehensive' entails and doesn't distinguish this from potential sibling tools like 'component_analyze' or 'playwright_test_ui' that might also analyze aspects of user experience. It provides a basic purpose but lacks specificity.
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 offers no guidance on when to use this tool versus alternatives. There are no explicit when/when-not instructions or references to sibling tools, leaving the agent to infer usage from the name alone. This lack of context makes it difficult to select appropriately among the server's analysis-related 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'optimize and deduplicate' but does not explain what this entails (e.g., whether it modifies input, requires specific permissions, has rate limits, or returns structured output). For a tool with two parameters and no output schema, this leaves significant gaps in understanding its behavior and effects.
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 with a single phrase, 'Optimize and deduplicate Tailwind classes', which is front-loaded and wastes no words. Every part of the sentence directly contributes to the purpose, making it efficient and well-structured despite its brevity.
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 has two parameters, no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It does not address input/output behavior, error handling, or practical use cases, leaving users with insufficient context to effectively invoke the tool or understand its results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It does not mention the 'html' parameter (required) or the 'purge' parameter (optional with default), nor does it explain their roles (e.g., 'html' as input content, 'purge' for removing unused classes). With two parameters and no schema descriptions, the description adds minimal value beyond the tool name.
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's purpose as 'Optimize and deduplicate Tailwind classes', which is clear but vague. It specifies the action ('optimize and deduplicate') and resource ('Tailwind classes'), but lacks details on scope (e.g., HTML input) or how it distinguishes from siblings like 'tailwind_generate_config' or 'workflow_optimize_ux'. This makes it minimally adequate but not specific enough for full differentiation.
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 context (e.g., for cleaning up HTML), prerequisites, or exclusions, nor does it reference sibling tools like 'component_analyze' or 'workflow_optimize_ux' that might overlap. Without such information, users must infer usage from the purpose 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 full burden. It states 'create' which implies a write/mutation operation, but doesn't disclose behavioral traits like whether this generates code, modifies files, requires specific permissions, has side effects, or what the output looks like. For a creation tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that gets straight to the point with zero wasted words. It's front-loaded with the core purpose and includes the key constraint (Framer Motion or GSAP). Every word earns its place in this minimal description.
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 a creation tool with 2 parameters (one complex array), 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what gets created (code? configuration file?), how to use the output, what the animations array should contain, or any behavioral context. For a tool that presumably generates animation timelines, this leaves too many unanswered questions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'Framer Motion or GSAP' which relates to the 'library' parameter with enum values, but doesn't explain what this choice means. It doesn't address the 'animations' array parameter at all - no explanation of what animations are, how they're structured, or what the nested properties mean. The description adds minimal value beyond what the bare schema provides.
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 'create' and the resource 'animation timeline', specifying it works with Framer Motion or GSAP. It distinguishes from some siblings like animation_preview or component_analyze by focusing on creation rather than previewing or analyzing. However, it doesn't explicitly differentiate from component_create or workflow tools, which might also involve creation tasks.
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 when to choose Framer Motion vs. GSAP, nor does it explain when to use this versus sibling tools like animation_preview or component_create. There's no context about prerequisites, typical use cases, or exclusions.
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 preview' but doesn't specify whether this is a read-only operation, if it requires specific permissions, what the output looks like (e.g., file format, size), or any side effects like resource consumption. This is inadequate for a tool with no annotation coverage.
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 with a single sentence ('Generate preview of animation sequence'), front-loaded and without any wasted words. Every part of the sentence contributes directly to the tool's purpose, 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 complexity (2 parameters including a nested object), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't address key aspects like parameter usage, output format, or behavioral traits, leaving significant gaps for the agent to understand how to invoke the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no meaning beyond the input schema, which has 0% description coverage. It doesn't explain what 'timeline' or 'format' parameters represent, their expected structure, or how they affect the preview generation. With two parameters and no schema descriptions, the description fails to compensate for the coverage gap.
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 preview') and the resource ('animation sequence'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'animation_create_timeline' or 'playwright_capture_screenshots', which might also involve animation or preview generation in different contexts.
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. There are no explicit instructions on prerequisites, context, or comparisons to sibling tools such as 'animation_create_timeline' or 'playwright_capture_screenshots', leaving the agent to infer usage based on the name 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?
With no annotations provided, the description carries full burden but only states it creates with 'best practices' without detailing what that entails (e.g., file structure, dependencies, permissions, or mutation effects). It lacks behavioral context like error handling, side effects, or output format, leaving significant gaps for a creation tool.
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 no wasted words, front-loading the core purpose. It's appropriately sized for the tool's complexity, though it could benefit from more detail given the gaps in other dimensions.
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 creation tool with 5 parameters (including nested objects), 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't address parameter usage, behavioral traits, or output expectations, making it inadequate for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but adds no parameter details. It doesn't explain what 'name', 'type', 'props', 'styles', or 'accessibility' mean or how they're used, failing to provide meaning beyond the bare schema with nested objects and enums.
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 ('Create') and resource ('new UI component'), specifying it's for UI components with best practices. It distinguishes from siblings like component_analyze (analysis) and workflow tools, but doesn't explicitly differentiate from other creation tools like animation_create_timeline.
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?
No guidance on when to use this tool versus alternatives is provided. The description mentions 'best practices' but doesn't specify prerequisites, constraints, or when to choose this over other component-related tools like storybook_get_stories or workflow_build_design_system.
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 for behavioral disclosure. While 'capture' implies a read operation, it doesn't specify whether this requires network access, what happens with invalid URLs, whether it respects authentication, or what the output format looks like. The description lacks essential behavioral context for a tool that interacts with external resources.
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 5 words, with zero wasted language. It's front-loaded with the core action and scope. Every word earns its place in conveying the essential purpose.
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 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how errors are handled, or provide any context about the parameters. The description leaves too many unanswered questions for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 3 parameters (1 required), the description provides no information about parameters. It doesn't mention the 'url' parameter at all, nor does it explain what 'viewports' or 'fullPage' mean in context. The description fails to compensate for the complete lack of schema 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 action ('capture screenshots') and scope ('across browsers and viewports'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'playwright_test_ui' or 'storybook_test_component' that might also involve browser interaction or visual testing.
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. There's no mention of prerequisites, when-not-to-use scenarios, or comparisons with sibling tools like 'playwright_test_ui' or visual testing tools in the sibling list.
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. 'Run UI tests' implies execution and potential side effects, but it doesn't describe what happens during testing (e.g., browser automation, network activity), whether it modifies systems, what output to expect, or error handling. This is inadequate for a tool with execution capabilities.
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 with just four words, front-loading the core purpose without any wasted language. It's appropriately sized for a simple statement, though this brevity contributes to gaps in other dimensions.
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 (3 parameters, execution behavior, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns, how tests are structured, what 'actions' and 'assertions' arrays contain, or the execution environment. This leaves significant gaps for an AI agent to understand and use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but fails to do so. It mentions no parameters at all, leaving 'url', 'tests', and 'browsers' completely unexplained. The description doesn't clarify what 'url' refers to, what 'tests' structure entails, or how 'browsers' affect execution.
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 'Run UI tests with Playwright' clearly states the action (run) and resource (UI tests) with the specific framework (Playwright). It distinguishes from siblings like 'playwright_capture_screenshots' by focusing on testing rather than screenshot capture. However, it doesn't fully differentiate from 'storybook_test_component' which might also involve UI testing.
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 when to choose this over 'storybook_test_component' for component testing or 'playwright_capture_screenshots' for visual testing. There's no context about prerequisites, testing scenarios, or integration with 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?
No annotations are provided, so the description carries the full burden. It mentions 'Run visual and accessibility tests' but lacks critical behavioral details: whether this is a read-only or destructive operation, what permissions are needed, how results are returned, or any rate limits. For a testing 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 with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration, earning its place as 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 (testing tool with 3 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It doesn't explain return values, error handling, or behavioral traits, leaving the agent with insufficient information to use the tool effectively beyond its basic purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds no meaning beyond the schema—no explanation of what componentName, storyName, or testTypes represent, their formats, or how they interact. With 3 parameters (one required) and no enum details in the description, this fails to provide necessary context.
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 with specific verbs ('Run visual and accessibility tests') and resource ('on a component'), distinguishing it from siblings like component_analyze or component_create. However, it doesn't explicitly differentiate from other testing tools like playwright_test_ui, which slightly reduces clarity.
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 playwright_test_ui and storybook_get_stories, there's no indication of context, prerequisites, or exclusions, leaving the agent to 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.
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 states the tool generates configuration but doesn't explain how it processes input (e.g., validation, defaults), what output format to expect, or any side effects like file creation. This leaves significant gaps for a tool with complex nested parameters.
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 appropriately sized and front-loaded, making it easy to understand at a glance.
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 (4 nested parameters, 0% schema coverage, no output schema, no annotations), the description is insufficient. It doesn't explain parameter usage, output format, or behavioral traits, leaving the agent with inadequate information to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It mentions 'design tokens' but doesn't explain the four parameters (colors, spacing, typography, breakpoints) or their expected structures. This lack of detail fails to adequately supplement the bare schema.
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 configuration from design tokens.' It specifies the verb ('generate'), resource ('Tailwind configuration'), and source ('design tokens'). However, it doesn't explicitly differentiate from sibling tools like 'tailwind_optimize_classes' or 'workflow_build_design_system,' which prevents 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.
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 'tailwind_optimize_classes' for class optimization or 'workflow_build_design_system' for broader design system tasks, nor does it specify prerequisites or exclusions for usage.
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 states 'Generate' which implies a creation/mutation operation, but doesn't specify if this is a read-only analysis, a destructive overwrite, requires authentication, has rate limits, or what the output entails (e.g., file creation, API response). This is inadequate for a tool with potential side effects.
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 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover parameter meanings, behavioral traits, or output details, leaving significant gaps for the agent to understand how to invoke and interpret results effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 5 parameters are documented in the schema. The description adds no parameter semantics—it doesn't explain what 'source', 'sourceId', or the boolean flags mean, nor their impact on the generation process. This fails to compensate for the lack of schema 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 action ('Generate') and the target resource ('complete design system from components'), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'component_analyze' or 'storybook_get_stories' which might also relate to design systems or components, 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?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't explain if this is for initial setup versus updates, or how it differs from sibling tools like 'component_create' or 'tailwind_generate_config' that might overlap in design system creation. This leaves the agent without context for 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 for behavioral disclosure but only states the basic action. It doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what the returned list structure looks like (e.g., format, pagination). This leaves significant gaps for a tool that fetches data.
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 wasted words, front-loading the core purpose ('Get list of all Storybook stories'). It achieves maximum clarity in minimal space, making it easy for an agent 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 lack of annotations and output schema, the description is insufficiently complete. It doesn't explain the return format (e.g., JSON array, story metadata), error conditions, or behavioral nuances, leaving the agent under-informed for a data-fetching tool in a development context with multiple sibling tools.
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%, with the single parameter 'url' clearly documented in the schema. The description adds no additional parameter context beyond implying a Storybook URL is needed, so it meets the baseline for adequate but unenhanced 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 action ('Get list') and resource ('all Storybook stories'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'storybook_test_component' or specify what constitutes a 'story' in Storybook context, preventing 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.
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 'storybook_test_component' or other sibling tools. It lacks context about prerequisites (e.g., needing a running Storybook instance) or typical use cases, leaving the agent with minimal usage direction.
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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