Generative UI MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear, distinct purpose of loading design guidelines for UI generation.
Naming Consistency5/5The single tool name follows a clear verb_noun pattern (load_ui_guidelines), and with only one tool, consistency is inherently perfect as there are no other names to compare against.
Tool Count2/5One tool is too few for a server named 'Generative UI MCP', which suggests a broader scope of generating visual widgets. The tool only loads guidelines, lacking actual generation, editing, or management tools, making the set feel incomplete and thin for the implied purpose.
Completeness1/5The server is severely incomplete for generative UI tasks. It only provides guidelines loading, with no tools for creating, updating, deleting, or rendering widgets, leaving significant gaps that will cause agent failures in generating visual content.
Average 3.7/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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- No stable releases found
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- 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
- 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 mentions that the tool loads guidelines, implying a read-only operation, but doesn't disclose behavioral traits like whether it caches data, requires authentication, has rate limits, or what happens on repeated calls. The description adds some context (prerequisite timing) but lacks comprehensive behavioral details for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: two sentences that efficiently convey purpose, usage timing, and available modules. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers purpose and usage timing but lacks details on output format, error handling, or behavioral traits. Without annotations or output schema, more context would be helpful for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the 'modules' parameter fully documented in the schema (including enum values and descriptions). The description adds minimal value beyond the schema by listing the available modules, but doesn't provide additional semantics like usage examples or constraints. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Load detailed design guidelines for generating visual widgets.' It specifies the verb ('Load') and resource ('detailed design guidelines'), and mentions the target use case ('generating visual widgets'). However, since there are no sibling tools, it doesn't need to differentiate from alternatives, 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 Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage guidance: 'Call this before generating your first widget in a conversation.' This indicates when to use the tool (as a prerequisite step). It also lists available modules, which helps users understand scope. However, it doesn't explicitly state when not to use it or compare to alternatives, and with no sibling tools, this is less critical.
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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