Skip to main content
Glama
robertZaufall

MindManager MCP Server

serialize_current_mindmap_to_json

Convert the active mind map to structured JSON data with customizable detail levels for integration and analysis.

Instructions

Serializes the currently loaded mindmap to a detailed JSON object with ID mapping.

Args:
    ignore_rtf (bool): Whether to ignore RTF content. Defaults to True.
    mode (str): Detail level ('full', 'content', 'text'). Defaults to 'full'.
    turbo_mode (bool): Enable turbo mode (text only). Defaults to False.

Returns:
    Union[Dict[str, Any], Dict[str, str]]: JSON serializable dictionary or error dictionary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ignore_rtfNo
modeNofull
turbo_modeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The primary FastMCP tool handler decorated with @mcp.tool(). It handles parameters, calls the internal _serialize_json helper, serializes the result, and manages errors. This is both the implementation and registration of the tool.
    @mcp.tool()
    async def serialize_current_mindmap_to_json(
        ignore_rtf: bool = True,
        mode: str = 'full',
        turbo_mode: bool = True
    ) -> Union[Dict[str, Any], Dict[str, str]]:
        """
        Serializes the currently loaded mindmap to a detailed JSON object with ID mapping.
    
        Args:
            ignore_rtf (bool): Whether to ignore RTF content. Defaults to True.
            mode (str): Detail level ('full', 'content', 'text'). Defaults to 'full'.
            turbo_mode (bool): Enable turbo mode (text only). Defaults to False.
    
        Returns:
            Union[Dict[str, Any], Dict[str, str]]: JSON serializable dictionary or error dictionary.
        """
        try:
            print(f"Serializing current mindmap to detailed JSON (ignore_rtf={ignore_rtf}).", file=sys.stderr)
            json_obj = _serialize_json(ignore_rtf=ignore_rtf, mode=mode, turbo_mode=turbo_mode)
            print("Serialization to detailed JSON successful.", file=sys.stderr)
            return json_obj
        except Exception as e:
            print(f"ERROR during serialization to JSON: {e}", file=sys.stderr)
            return {"error": "Serialization Error", "message": f"Failed to serialize to JSON: {e}"}
  • Supporting helper function that creates a MindmapDocument instance, retrieves the mindmap, and uses serialization.serialize_object_simple to produce the JSON output.
    def _serialize_json(ignore_rtf=True, mode='content', turbo_mode=False):
        document = _get_document_instance(turbo_mode=turbo_mode)
        if document.get_mindmap(mode=mode):
            json_obj = serialization.serialize_object_simple(document.mindmap, ignore_rtf=ignore_rtf)
            return json_obj
        return None
Behavior3/5

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

With no annotations provided, the description carries full burden. It describes the core behavior (serialization to JSON) and mentions error handling in the return type, but lacks details about permissions, rate limits, or what happens if no mindmap is loaded. The description doesn't contradict any annotations.

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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value: the first establishes core functionality, parameter explanations clarify usage, and return statement manages expectations.

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 3 parameters with no schema descriptions and an output schema present, the description provides good parameter semantics and mentions return types. It could be more complete by explaining what 'currently loaded' means or error conditions, but covers the essentials for a serialization tool.

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?

With 0% schema description coverage, the description compensates by explaining all 3 parameters: 'ignore_rtf' controls RTF content handling, 'mode' specifies detail level with enum values, and 'turbo_mode' enables text-only mode. This adds meaningful context beyond the bare schema.

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 ('serializes'), target resource ('currently loaded mindmap'), and output format ('detailed JSON object with ID mapping'). It distinguishes from siblings like 'serialize_current_mindmap_to_markdown' and 'serialize_current_mindmap_to_mermaid' by specifying JSON output.

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 needing JSON serialization of a loaded mindmap, but doesn't explicitly state when to use this tool versus alternatives like markdown or mermaid serialization. No explicit exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/robertZaufall/mindm-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server