ui-architect-mcp

Server Quality Checklist

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Latest release: v2.1.0
Disambiguation5/5
Each tool has a clear and distinct purpose tied to a specific phase in the design pipeline (analysis, planning, theme, component selection, component exploration, background generation, SEO audit). There is no overlap — even the two component tools (select_components and explore_components) are differentiated by source (built-in vs. community).
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with underscores (e.g., analyze_project, design_theme, select_components). The naming is predictable and descriptive, making it easy for an agent to understand each tool's role.
Tool Count5/5
With 7 tools, the set is well-scoped for a UI architecture design process. Each tool earns its place, covering the essential phases without redundancy or unnecessary complexity.
Completeness4/5
The tool surface covers the full design pipeline from analysis to SEO audit. However, the seo_audit description references a 'generate_full_page' tool that is not included, and there is no explicit review tool, leaving a minor gap in the workflow.
Average 4.3/5 across 7 of 7 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
- Last stable release on March 25, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses that the tool uses stored data from a previous step and generates multiple outputs. However, it does not discuss side effects, idempotency, or error conditions, leaving some behavioral unknowns.
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 concise, using bullet points for clarity and an emphasized note for the prerequisite. It front-loads the phase and role, making it easy to scan. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description covers the generated items thoroughly. However, it omits details about the return format or how the outputs are structured, which would help an agent fully understand the tool's behavior.
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 each parameter already has a description. The tool description does not add significant new insight into parameter usage beyond repeating the override behavior. It adds marginal value.
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 identifies the tool as a system architect phase that generates technology stack decisions, component maps, file structure, interactivity plan, and routing strategy. It specifies the input (project scope from analyze_project) and outputs, distinguishing it from sibling tools like design_theme or select_components.
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 explicitly states the prerequisite: 'Run analyze_project first.' This provides clear context for when to use the tool. It does not list when not to use it or alternatives, but the single important guideline is well communicated.
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 discloses that patterns are 'barely noticeable' and auto-selected based on industry, and lists pattern types. However, it doesn't detail response format (e.g., whether it returns a CSS string or file), potential errors, or performance implications. Adequate but not thorough.
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 front-loaded with the tool's purpose, followed by return value, pattern types, and important note. It is concise without unnecessary verbosity. The 'Phase 4' label might be slightly redundant but doesn't detract significantly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the input schema covers all parameters and there is no output schema, the description adequately explains the tool's function, auto-selection logic, and prerequisite. It could be more complete by describing edge cases (e.g., invalid industry) but is sufficient for most use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds meaning by explaining how pattern types correspond to industries (e.g., 'geometric' for tech/corporate) and that style is an override on auto-selection. This contextualizes the parameters beyond the schema's brief descriptions.
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 it generates a CSS background pattern matching industry and theme, specifying it's production-ready CSS for subtle patterns. It distinguishes from siblings by mentioning 'Phase 4' and the prerequisite of design_theme, making its role in the sequence explicit.
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 context: it's Phase 4 and requires design_theme to be run first so colors match. It lists pattern types and their associations, guiding when each might be appropriate. However, it doesn't explicitly state when not to use this tool or mention alternatives beyond the prerequisite.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses return values (code, token registry, accessibility notes), and notes high animation preference with specific effects. It does not mention side effects or permissions, but for a code-generation tool this is adequate.
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 somewhat wordy with a bullet list and emphasis, but it is well-structured with clear sections (phase, returns, features, prerequisites, presets). Each sentence adds value, though it could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description thoroughly explains return values, provides component count and styles, covers all parameters with presets, and includes a prerequisite. It is comprehensive for a component selection tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description adds significant value beyond schema: explains style auto-resolution, lists framework options, defines presets, and specifies animation preference default. This enriches parameter understanding.
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 'Select and adapt animated UI components for your framework and design system', with a specific verb-resource pair and differentiation from siblings like explore_components which likely is for browsing. The phase numbering also provides context.
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 a prerequisite ('Run design_theme first') and lists presets, but does not explicitly state when to use this tool versus siblings like explore_components or generate_background. The guidance is partial.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 discloses that the tool analyzes, identifies, generates clarifying questions, and returns a structured output including scope, structure, questions, and risk assessment. It does not mention side effects, but the tool appears to be read-only and non-destructive, which is acceptable for analysis.
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 two clear paragraphs and an important callout. It front-loads the tool's role as Phase 1 and first call. There is minimal fluff, though 'Senior Project Manager Analysis' could be considered slightly redundant. Still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters, 1 required, no output schema, the description explains what the tool returns (scope, structure, questions, risk). It does not detail the exact format of the output, but it provides enough context for an agent to understand its purpose and usage. A slightly richer description of the output could push it to 5.
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 all parameters have individual descriptions in the schema. The overall description adds context about the tool's purpose and output but does not add parameter-specific semantics beyond what the schema already provides. Baseline 3 is appropriate.
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 analyzes a project description to produce a structured scope document, identifying project type, industry, tone, etc. It explicitly positions itself as the first tool to call, distinguishing it from siblings like generate_background or plan_architecture.
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 'THIS SHOULD BE THE FIRST TOOL CALLED' and 'Always call this FIRST before any other tool. It sets up the entire project context.' This provides clear guidance on when to use it, and it implies it should be used before any other tool. No misuse scenario is stated, but given its role, it is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that theme selection is never random and is based on industry, and that colors avoid typical AI gradients. This adds meaningful behavioral context beyond the schema, though it could mention permissions or 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 concise and well-structured: first sentence states action, then lists return values, then explains behavioral nuance, then provides ordering. Every sentence is informative with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (generating a design system) and no output schema, the description adequately covers return values, selection logic, and ordering. It omits potential error cases or prerequisites (like requiring prior phases), but overall it is sufficiently complete for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with all parameters described. The description adds value by explaining that brandColor is optional and that themePreference allows forcing light/dark, with 'auto' recommended. It also gives examples for industry and tone, enriching meaning beyond the schema.
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 generates a complete design system from business context. It lists what is returned (color palette, typography, etc.) and distinguishes from siblings by specifying its phase order (after analyze_project and plan_architecture, before select_components).
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 explicitly states the call order relative to sibling tools (AFTER analyze_project and plan_architecture, BEFORE select_components), providing clear context. However, it does not include exclusions or when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes retrieval from external source, return values (HTML/CSS, scores, URLs), and ranking by animation. Implies read-only; no annotations provided, but description is adequate for 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise (~150 words), well-structured, front-loaded with core purpose. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers main functionality, return values, and usage context. Lacks details on constraints like rate limits but compensates for missing output schema with description of return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions; description adds context on ranking, integration with select_components, and category list, enriching parameter understanding.
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 fetches animated UI components from UIverse's open-source library, distinguishes from built-in components, and lists categories. It goes beyond a tautology with specific verb and resource.
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?
Explicitly says to use for discovering beyond built-in library and to combine with select_components, providing clear guidance on when to use this tool vs sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It clearly explains the tool is a read-only analysis (audit) that returns results and suggests fixes for a feedback loop. It does not mention any destructive actions or authentication needs, which is acceptable for an audit tool. However, it could explicitly state that it does not modify any 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 concise and well-structured, starting with the phase number and title. It uses bullet-like formatting for the categories and return values, making it easy to scan. Every sentence adds value, and there is no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the parameter count (7 with 100% schema coverage) and no output schema, the description provides sufficient detail about return values (score, grade, issue list with severity and fix instructions, top 5 recommendations). It also explains the pipeline integration and feedback loop. The tool is fully specified for its purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds significant value beyond the schema. For example, it explains that 'code' should be 'the complete page HTML from generate_full_page', and that 'js' and 'css' are for separate files. It provides context for parameters like 'industry' and 'pageType' for SEO recommendations, and explains the purpose of 'targetKeywords' for checking in headings and content.
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: analyzing generated HTML for SEO optimization and digital marketing readiness. It specifies it checks 50+ factors across 10 categories and returns a score, grade, issues, and recommendations. It also explicitly positions the tool in a pipeline: 'Call this AFTER generate_full_page, BEFORE review_output.' This distinguishes it from sibling tools like generate_background or plan_architecture.
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 provides explicit when-to-use guidance: after generate_full_page and before review_output. It also explains the feedback loop: if the score is too low, re-run code generation with fixes. It mentions using industry and pageType parameters for context-aware checks, and suggests targetKeywords for checking. No alternatives are needed as siblings are unrelated.
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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