MCP UI Glue Code Generator
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 tool's purpose is clearly distinct and singular, making misselection impossible.
Naming Consistency5/5The single tool name follows a clear verb_noun pattern (generate_ui_schema). Since there is only one tool, consistency is inherently perfect with no deviations to assess.
Tool Count2/5A single tool is too few for a server named 'MCP UI Glue Code Generator', which suggests a broader scope involving UI generation tasks. This minimal set feels thin and underdeveloped for the implied purpose.
Completeness2/5The tool surface is severely incomplete for the domain. While generate_ui_schema handles schema generation and preview, there are obvious gaps such as validation, customization, integration with other UI frameworks, or management of generated schemas, limiting agent workflows.
Average 3.7/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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
- 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 of behavioral disclosure. It describes key behaviors: mapping JSON to component props, generating schema code, providing a live UI preview, and optional file saving. However, it lacks details about error handling, performance characteristics, authentication needs, or rate limits. For a tool with no annotations, this provides basic behavioral context but leaves gaps.
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 highly concise and well-structured in two sentences. The first sentence front-loads the core functionality (mapping and returns), while the second adds an optional feature (saving). Every word contributes directly to understanding the tool's capabilities without redundancy or fluff.
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 (3 parameters, no output schema, no annotations), the description is somewhat complete. It covers the main transformation purpose and optional saving, but lacks details on output format (beyond 'schema code' and 'UI preview'), error cases, or integration constraints. Without an output schema, more explanation of return values would be beneficial, making this minimally adequate.
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?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'messy API JSON' (hinting at the nature of api_json_sample) and 'Optional path to save the HTML preview file' (clarifying output_path's purpose). This meets the baseline for high schema coverage but doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does 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 ('Maps', 'Returns', 'Optionally saves') and resources ('messy API JSON', 'Vue/React Design System component props', 'Zod schema', 'generated schema code', 'live UI preview', 'HTML file'). It distinguishes what the tool does from generic transformation tools by specifying the exact input/output mapping and technologies involved.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through phrases like 'Maps messy API JSON to Vue/React Design System component props' and 'Optionally saves preview as HTML file', suggesting it's for frontend development workflows. However, there are no explicit guidelines about when to use this tool versus alternatives, prerequisites, or limitations. With no sibling tools, this is adequate but lacks explicit guidance.
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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