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eDramas

MasterGo Magic MCP

by eDramas
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.0.4-beta.3

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves the overall DSL data and code generation rules, the other retrieves component documentation from links found in the DSL. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with the 'mcp__' prefix (getDsl, getComponentLink), maintaining naming uniformity.

    Tool Count2/5

    With only 2 tools, the server feels undersized for a design-to-code workflow. Typically, a design tool MCP would require 3-15 tools to cover essential operations like listing files, managing components, or triggering generation.

    Completeness3/5

    The tools cover data retrieval (DSL and component documentation) but lack operations for actually generating code, managing files, or updating designs. Notable gaps exist for a complete code generation pipeline.

  • Average 4/5 across 2 of 2 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 ISC License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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?

    With no annotations, the description must disclose all behavioral traits. It mentions sequential retrieval but does not describe side effects, error handling for invalid URLs, or rate limits. This is adequate but not rich.

    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 two sentences, front-loaded with the usage condition, and contains no unnecessary words. Every sentence serves a purpose.

    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 simplicity (one parameter, no output schema) and lack of annotations, the description adequately covers purpose, usage condition, and parameter source. It could detail return value structure but notes its use for code generation.

    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 coverage is 100% and the parameter description in the schema already explains the URL source and validity. The tool description adds little new semantic value beyond restating the source.

    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 retrieves component documentation data using URLs from the componentDocumentLinks array, distinguishing it from its sibling mcp__getDsl which returns DSL data. The verb 'retrieve' and resource 'component documentation data' are specific.

    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 specifies the condition for use: when mcp__getDsl returns a non-empty componentDocumentLinks array. It implies sequential retrieval but does not specify when not to use it or mention alternatives beyond the sibling.

    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?

    With no annotations, the description carries the burden of behavioral disclosure. It states the tool returns DSL data and rules, implying a read-only operation, but does not explicitly confirm no side effects, permissions, or rate limits.

    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?

    Six sentences, each adding value: purpose, use cases, requirements, return format, additional content, and extensibility. Front-loaded with the main action.

    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 no output schema, the description explains the return value (DSL data in JSON, rules) and mentions transformation use. Could briefly mention expected JSON structure or limitations, but overall sufficient.

    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 coverage is 100% with clear descriptions of fileId and layerId. The description only reiterates the requirement to provide both, adding no new 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 retrieves DSL data from MasterGo design files and the rules for code generation, with specific use cases. It distinguishes from sibling tool getComponentLink by focusing on design structure analysis.

    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?

    Provides clear context for when to use the tool (analyze structure, hierarchy, properties) but does not explicitly mention when not to use it or compare to the sibling tool.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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