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jinzcdev

Markmap MCP Server

by jinzcdev
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TypeScript
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • 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.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (markdown_to_mindmap), establishing a consistent naming convention for the server.

    Tool Count2/5

    One tool is too few for a server's typical scope, as it limits functionality and may indicate an incomplete or overly narrow implementation. However, it is not as extreme as a trivial single tool, so it avoids the lowest score.

    Completeness3/5

    The tool provides a core conversion function, but there are notable gaps such as missing operations for editing, saving, or loading mind maps, which could hinder agent workflows. The surface is functional but not fully comprehensive.

  • Average 3.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 6 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

    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

  • Behavior2/5

    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 states the conversion action but fails to describe key behavioral traits such as what format the mind map output takes (e.g., file, URL, visual representation), whether the conversion is reversible, any rate limits, or error handling. This leaves significant gaps for an agent to understand how to interact with the tool effectively.

    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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded with the core action, making it easy to parse and understand quickly.

    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 the complexity of a conversion tool with no annotations and no output schema, the description is incomplete. It lacks details on the output format (e.g., how the mind map is returned or accessed), potential side effects, or any prerequisites for successful conversion. This leaves the agent with insufficient context to use the tool reliably.

    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 description coverage is 100%, so the input schema already documents both parameters ('markdown' and 'open') thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples of Markdown content or implications of the 'open' parameter. Baseline 3 is appropriate as the schema handles the heavy lifting.

    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 specific action ('Convert') and the resources involved ('Markdown document' into 'interactive mind map'), leaving no ambiguity about what the tool does. It distinguishes the transformation process explicitly, which is sufficient even without sibling tools for comparison.

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

    Usage Guidelines3/5

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

    The description implies usage when converting Markdown to a mind map, but provides no explicit guidance on when to use this tool versus alternatives (e.g., other conversion tools or manual methods). Since there are no sibling tools listed, this omission is less critical, but it still lacks proactive usage context.

    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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