shadcn-vue-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes: component-builder retrieves documentation, component-quality-check validates quality, components-filter filters components, component-usage-doc reads usage documentation, and requirement-structuring analyzes user requirements. However, component-builder and component-usage-doc both involve documentation retrieval, which could cause some confusion, though their specific focuses differ (builder for generation prep vs. doc for usage details).
Naming Consistency4/5
The naming follows a consistent pattern with a 'component-' prefix for four tools and a 'requirement-' prefix for one, all using kebab-case. While this is mostly consistent, the deviation with 'requirement-structuring' breaks the 'component-' pattern, though it still maintains readability and a clear verb-noun structure.
Tool Count5/5
With 5 tools, the count is well-scoped for a server focused on shadcn-vue component management. Each tool serves a specific role in the component lifecycle, from requirement analysis to quality checking, without being overly sparse or bloated, fitting typical MCP server ranges.
Completeness4/5
The tool set covers key aspects of component handling: requirement analysis, documentation retrieval, filtering, quality checking, and usage documentation. However, there are minor gaps, such as no explicit tools for generating or updating components directly (component-builder only retrieves docs), which agents might need to work around by integrating with other systems.
Average 3.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 2 community issues answered or closed 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 status not available
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
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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 full burden. It mentions 'filter components' but doesn't disclose behavioral traits like whether this is a read-only operation, what happens to filtered results (e.g., returned, modified, deleted), authentication needs, rate limits, or error conditions. The description lacks essential context for safe and effective use.
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 brief with two sentences, front-loading the purpose and then usage guidance. There's no wasted text, but it could be more structured (e.g., separating purpose from usage more clearly). It's appropriately sized for a simple tool, though slightly terse.
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, no output schema, and a single parameter with good schema coverage, the description is incomplete. It doesn't explain what the tool returns, how filtering works, or the relationship with sibling tools like 'requirement-structuring'. For a tool with implied complexity (filtering components with specific technologies), more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter ('message') documented as 'requirement json from requirement-structuring tool'. The description adds no parameter information beyond what the schema provides. With high schema coverage, the baseline is 3, but the description doesn't compensate with additional context about parameter format or usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'filter components with shadcn/ui components and tailwindcss' which provides some purpose (filtering components) but is vague about what 'filter' means operationally. It doesn't specify what resource is being filtered (components from where?) or distinguish from sibling tools like 'component-builder' or 'component-quality-check'. The description partially restates the name 'filter' without adding clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes 'Use this tool when mentions /filter' which provides implied usage context based on a trigger phrase. However, it doesn't explicitly state when NOT to use this tool, what alternatives exist among the sibling tools, or clarify prerequisites. The guidance is minimal and relies on pattern matching rather than functional differentiation.
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 for behavioral disclosure. It implies a read-only operation ('read usage doc'), but doesn't specify what the output contains (e.g., documentation text, links, examples), whether it requires authentication, or if there are rate limits. For a tool with no annotations, this leaves significant behavioral gaps, though it correctly indicates a read action.
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 very concise with two short phrases, making it easy to parse. However, the second phrase 'Use this tool when mentions /doc' is somewhat cryptic and could be clearer. Overall, it's front-loaded with the core purpose and wastes no words, earning a high score for efficiency despite minor clarity issues.
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 the tool returns (e.g., documentation content, format, or errors), which is critical for a read operation. With 2 parameters fully covered by the schema, the main gap is output behavior, making this inadequate for agent use without additional context.
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 clear descriptions for both parameters ('type' with enum values and 'name' as component name in lowercase). The description adds no parameter-specific information beyond what the schema provides, such as examples or usage tips. Given high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.
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 'read usage doc of a component' which provides a clear verb ('read') and resource ('usage doc of a component'), establishing the basic purpose. However, it doesn't distinguish this tool from potential siblings like 'component-quality-check' or 'requirement-structuring' that might also involve documentation or component analysis. The purpose is clear but lacks sibling 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 includes 'Use this tool when mentions /doc', which gives a specific trigger but no broader context. It doesn't explain when to use this versus alternatives (e.g., 'component-quality-check' for quality issues or 'requirement-structuring' for requirements), nor does it provide exclusions or prerequisites. This is minimal guidance that doesn't help the agent choose between sibling 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 states the tool retrieves documentation and returns text snippets, but it doesn't disclose critical behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what happens if inputs are invalid. The description adds some context about the output being 'text snippets' and the need for subsequent integration, but significant gaps remain 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loaded with the main purpose, but it includes redundant phrasing ('Retrieve documentation for all filtered components and charts to prepare for component generation, This tool ONLY returns the text snippet for that UI component') that could be more concise. The second sentence adds workflow guidance, which is useful but not strictly necessary for the tool's core function. Overall, it's adequately structured but could be tighter.
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 (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, error handling, and the format or content of the returned text snippets. While it mentions the tool's role in a workflow, it doesn't provide enough context for an AI agent to fully understand how to use it effectively, especially with no output schema to clarify return values.
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 (icon, components, charts) with descriptions and enums. The description doesn't add any meaningful semantic details beyond what the schema provides, such as explaining the relationship between components and charts or how the icon parameter affects 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 tool's purpose: 'Retrieve documentation for all filtered components and charts to prepare for component generation.' It specifies the verb ('retrieve'), resource ('documentation for components and charts'), and context ('prepare for component generation'). However, it doesn't explicitly differentiate from sibling tools like 'component-usage-doc' or 'components-filter', which likely have related purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating 'This tool ONLY returns the text snippet for that UI component' and 'After calling this tool, you must edit or add files to integrate the snippet into the codebase,' which suggests it's part of a workflow. However, it doesn't explicitly state when to use this tool versus alternatives like 'component-usage-doc' or 'components-filter,' nor does it provide clear prerequisites or exclusions. The guidance is implied but not comprehensive.
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. While it mentions the tool provides 'detailed feedback,' it doesn't describe the format, depth, or limitations of that feedback (e.g., is it a report, score, or list of issues?). For a quality-check 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that directly address purpose and usage. The first sentence clearly states what the tool does, and the second provides usage guidelines. There's no wasted text, though the 'or when mentions /check' could be integrated more smoothly.
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 quality-check tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the feedback looks like (e.g., format, structure, or examples), which is critical for understanding the tool's output. This gap makes it harder for an AI agent 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?
The schema description coverage is 100%, with the single parameter 'componentCode' fully documented in the schema. The description doesn't add any additional meaning or context about the parameter beyond what's in the schema, so it meets the baseline of 3 where 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: 'Automatically check Vue component quality and provide detailed feedback.' It specifies the verb ('check'), resource ('Vue component quality'), and scope of feedback (quality, accessibility, performance, best practices). However, it doesn't explicitly differentiate from sibling tools like 'component-builder' or 'component-usage-doc' beyond mentioning component-builder in the parameter description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage guidance: 'Use this tool when you need to validate component quality, accessibility, performance, and best practices compliance. or when mentions /check.' It specifies when to use the tool and includes a trigger ('mentions /check'), but doesn't explicitly state when not to use it or name alternatives among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 describes the transformation behavior (natural language to structured document) but lacks details about output format, processing limitations, error handling, or performance characteristics. The description adds value by explaining the transformation but doesn't provide comprehensive behavioral context.
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 appropriately sized with two sentences that each serve distinct purposes: the first explains the tool's function, the second provides usage guidelines. It's front-loaded with the core purpose. Minor improvement could be made by slightly tightening the example list.
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?
For a single-parameter tool with no annotations and no output schema, the description provides adequate purpose and usage context but lacks details about the output format, processing behavior, or limitations. Given the transformation nature of the tool and absence of output schema, more information about what the structured document looks like would be helpful.
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 the single 'message' parameter. The description adds context about what the parameter should contain ('user's natural language', 'user requirement in specific contextual information'), but doesn't provide additional syntax, format, or constraints beyond what the schema indicates.
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 tool's purpose with specific verbs ('analyze', 'structure') and resource ('requirements into a clear and structured component requirement document'). It distinguishes from siblings by focusing on requirement analysis rather than building, checking, filtering, or documenting components.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'when the user requests a new UI component' and provides concrete examples (mentions of /ui, button, input, dialog, etc.). It implicitly distinguishes from siblings by focusing on requirement structuring rather than other component-related tasks.
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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