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YuChenSSR

Mindmap MCP Server

by YuChenSSR
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
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 has a single, clearly defined purpose of converting Markdown to mindmaps, so an agent cannot misselect between multiple options.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (convert_markdown_to_mindmap), which is consistent within itself. There are no other tools to compare against, so naming consistency is inherently perfect.

    Tool Count2/5

    A single tool is too few for a server named 'Mindmap MCP Server', which suggests a broader mindmap-related domain. This minimal toolset feels thin and incomplete for the apparent scope, limiting functionality to just one conversion operation.

    Completeness2/5

    The tool surface is severely incomplete for a mindmap domain. While it covers conversion from Markdown, there are obvious gaps such as creating mindmaps from scratch, editing existing mindmaps, exporting to other formats, or managing mindmap files, which will likely cause agent failures in broader workflows.

  • Average 3.2/5 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.

  • 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 to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 mentions the return behavior (HTML content or file path based on server argument), which adds some context, but lacks details on error handling, performance, or side effects. For a tool with no annotations, this is insufficient to fully inform the agent.

    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 well-structured and concise, with a clear purpose statement followed by brief sections for arguments and returns. Each sentence serves a functional role without unnecessary elaboration, though minor redundancy ('mindmap mind map') slightly detracts from perfection.

    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 moderate complexity (one parameter, no annotations, but with an output schema), the description is reasonably complete. It covers the purpose, parameter meaning, and return behavior, and the presence of an output schema reduces the need to detail return values. However, it could benefit from more behavioral context to achieve full completeness.

    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 semantics beyond the input schema, which has 0% coverage. It explains that 'markdown_content' is 'The Markdown content to convert', clarifying the parameter's purpose. Since there is only one parameter and the schema provides no description, this compensation is effective, though not exhaustive.

    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?

    The description clearly states the tool's purpose: converting Markdown content to a mindmap. It specifies the verb 'convert' and the resource 'Markdown content', making the function unambiguous. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It only states what the tool does without context for its application, leaving the agent to infer usage scenarios independently.

    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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