Utilities
Generate table of contents and directory structure from text content using AI
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/text-directory-generator' \
-H 'Content-Type: application/json' \
-d '{"text":"Enter or paste the text content you want to analyze and generate a directory for...","format":"markdown","depth":3,"includeNumbers":true,"includeDescriptions":false,"style":"simple","language":"en"}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/text-directory-generator| Name | Type | Required | Description |
|---|---|---|---|
| text | textarea | Yes | — |
| format | select | No | — |
| depth | number | No | — |
| includeNumbers | checkbox | No | — |
| includeDescriptions | checkbox | No | — |
| style |
Add this tool to your Model Context Protocol server so AI agents can list and call it.
Add this block to your MCP client configuration:
{
"mcpServers": {
"elysiatools-text-directory-generator": {
"name": "text-directory-generator",
"description": "Generate table of contents and directory structure from text content using AI",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=text-directory-generator",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}After connecting to the SSE endpoint, list the exposed tools:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoke the tool by its id, passing arguments built from its parameters:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "text-directory-generator",
"arguments": {
"text": "Enter or paste the text content you want to analyze and generate a directory for...",
"format": "markdown",
"depth": 3,
"includeNumbers": true,
"includeDescriptions": false,
"style": "simple",
"language": "en"
}
}
}Questions or issues? Contact [email protected]
| select |
| No |
| — |
| language | select | No | — |
Stream result
data: {"chunk": "data: processed content 1", "type": "stream"}
data: {"chunk": "data: processed content 2", "type": "stream"}
data: {"type": "done"}