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jiandong0718

xmind-to-markdown-mcp

by jiandong0718
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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: v0.1.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one converts XMind to Markdown, the other returns raw JSON structure. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with underscores (convert_xmind_to_markdown, read_xmind_structure), making them predictable.

    Tool Count4/5

    With only 2 tools, the scope is narrow but appropriate for the specific purpose of reading and converting XMind files. It covers the essential operations without being overly sparse.

    Completeness4/5

    The tool set covers the core functionality of converting XMind to Markdown and inspecting its structure. While additional features like selective conversion could be added, it is sufficient for the stated purpose.

  • Average 3.8/5 across 2 of 2 tools scored. Lowest: 2.9/5.

    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.

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

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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

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations provided, so description carries full burden. It mentions supported data (hierarchy, notes, tags) but omits key behavioral traits such as file overwrite behavior, authentication needs, error handling, or output format details. Significant gaps remain.

    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?

    Description is very short (one sentence) and front-loads the main action. It is concise, though additional details could be included without significant bloat.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no output schema and no annotations, the description lacks sufficient context for correct tool usage. Missing details on output format, default behavior of output_path, error scenarios, and file system interaction.

    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 covers all 3 parameters with descriptions (100% coverage). The description adds no extra semantic value beyond schema, so baseline score of 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Description clearly states the tool converts XMind files to Markdown and lists supported features (hierarchy, notes, tags). It is specific about the verb and resource, but does not explicitly differentiate from sibling tool 'read_xmind_structure', though the difference is implied.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance on when to use this tool versus alternatives, no prerequisites, no when-not scenarios. The description merely states what the tool does without contextual usage advice.

    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?

    描述了返回格式（JSON）和排除行为（不转换），隐含只读性质。虽无显式安全声明，但对于简单读取工具已经足够。

    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?

    单句描述，关键信息前置，无冗余。每个部分都有价值。

    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?

    工具简单（1参数，无输出schema），描述覆盖了核心功能、返回值格式、与兄弟工具的差异，完整无缺。

    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?

    唯一参数（xmind_path）已在schema中描述，描述额外补充了路径类型（相对或绝对），提供超越schema的实用信息。

    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?

    明确说明工具行为：读取XMind文件并返回JSON结构化数据，不进行Markdown转换。与兄弟工具区分开来，动词和资源清晰。

    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?

    描述了使用场景（查看原始结构），并明确排除Markdown转换，暗示替代方案（convert_xmind_to_markdown）。指导性强。

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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