Turn outlines and hierarchical notes into interactive mind maps through a hosted remote MCP server.
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Average 4.6/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of overlap or confusion. The tool's description clearly defines its purpose.
The single tool name 'render_mindmap' follows a clear verb_noun pattern ('render' + 'mindmap'), which is consistent and self-explanatory.
With only one tool, the server feels minimal, but for a specialized mindmap rendering function, this is borderline acceptable. It could benefit from additional tools like edit or delete, but it is not extreme.
The tool fully covers its stated purpose of rendering a mindmap from markdown input. There are no missing operations for the narrow scope of visualization, though the server lacks broader mindmap management capabilities.
render_mindmapMind MapARender an interactive MCP app mind map when the user needs hierarchical structure shown visually instead of as prose. Use it for breaking down ideas, plans, study material, or systems into a root topic with nested branches; do not use it for tables, flowcharts, Mermaid/Graphviz diagrams, or plain text lists. Input mindmap_markdown must be a clean markdown tree with one # root heading and 2-space-indented bullet nesting. If the user gives prose, first reshape it into that hierarchy, then call this tool.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | Optional short title for the mind map | |
| mindmap_markdown | Yes | Markdown tree for a mind map. Preferred structure: one top-level heading for the root topic, then nested bullet points for branches and leaves using consistent 2-space indentation. Example: `# Product Launch Strategy - Marketing - Social Media - Twitter - LinkedIn - Email Campaign - Newsletter - Promo Codes - Engineering - QA Testing - Server Scaling - Final Deployment - Customer Support - Help Docs Updated - Support Training`. When the topic is suitable for emojis (e.g., marketing, events, social media, holidays, categories like food/travel), use relevant emojis in node names to make the diagram more appealing — like 📱 for Social Media or 🚀 for Launch. Avoid prose paragraphs, tables, fenced code blocks, and mixed list styles. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond this: input must be a clean markdown tree with one root heading and 2-space indentation, and it requires the agent to reshape prose before calling. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences: purpose, usage boundaries, and input requirements. Every sentence earns its place, is front-loaded with the main action, and the structure is efficient with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, when to use, when not to use, input format, and preprocessing. With annotations covering safety and schema covering parameters, only minor gaps remain (e.g., exact return value or error behavior), but these are not critical for a read-only rendering tool. Overall, it's complete enough for reliable agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed param descriptions and an example. The tool description raises this further by emphasizing the structural requirements ('one # root heading', '2-space-indented bullet nesting') and the need to reshape prose, plus emoji guidance in the schema. This adds meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource ('Render an interactive MCP app mind map') and clearly defines the purpose: showing hierarchical structure visually. It distinguishes itself from other formats by explicitly listing what it is not for (tables, flowcharts, Mermaid/Graphviz, lists), which is especially valuable given there are no sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit when-to-use ('when the user needs hierarchical structure shown visually'), when-not-to-use ('do not use it for tables, flowcharts, Mermaid/Graphviz diagrams, or plain text lists'), and even a preprocessing instruction ('If the user gives prose, first reshape it'). This fully covers the decision boundary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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