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gsccoelho

mcp-vue3-python

by gsccoelho
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Python
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Server Quality Checklist

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

  • Latest release: v0.0.8

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The agent will always select the correct tool.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The single name follows a clear pattern (verb_noun in Portuguese).

    Tool Count4/5

    The server is focused on generating Vue 3 structures, and one tool may suffice for that narrow purpose. However, it falls below the typical 3-15 tool range, making it slightly thin.

    Completeness3/5

    The tool generates only the base file structure for Molecules, Organisms, and Views. It lacks generation of services, stores, or Python backend components implied by the server name, leaving notable gaps for a full CRUD workflow.

  • Average 4/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit 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?

    The description reveals that the tool creates files ('cria automaticamente os arquivos base'), indicating a write operation. However, it does not disclose potential side effects such as overwriting existing files or required permissions, which are important for a generative 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise and front-loaded with the main purpose in the first sentence. The argument list is structured separately, which is clear and efficient. No unnecessary information is present.

    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?

    Given the tool has two simple parameters and no output schema, the description adequately covers the purpose but lacks details on the generated output structure or return value. It could mention if the tool returns a success message or lists created files.

    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?

    The description adds meaningful context to both parameters, including expected formats (PascalCase/camelCase for module_name, absolute path for src_path) and an example. Since the schema itself has no descriptions (0% coverage), this compensates well by guiding proper usage.

    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 Vue 3 CRUD structure, specifying the verb 'gerar' and the resource 'estrutura completa de um CRUD Vue 3'. It also lists the created file types (Molecules, Organisms, Views), leaving no ambiguity about the tool's function.

    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 when to use the tool ('Utilize esta tool para gerar...'), covering the primary use case. However, it does not mention when not to use it or any prerequisites, though the lack of alternative tools makes this 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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  • 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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