Prompt

Retrieve essential Directus schema information to understand the data structure - collections, fields, and relationships. This is a READ-ONLY discovery tool designed to help you explore and comprehend existing schema. It is for schema exploration and understanding only. For schema modifications, use the dedicated collections, fields, and relations tools.

Note: This tool provides an extremly curated response optimized for LLM understanding, not the raw Directus API schema response.

Operation Modes

Discovery Mode (Default)

Usage: Call without parameters or with empty keys array

{}

Returns: Lightweight schema overview

• collections: Alphabetically sorted array of real collection names (database tables)
• collection_folders: Alphabetically sorted array of folder names (UI-only, not real tables). Distinct from file folders. They are used for grouping different collections together in the UI.
• notes: Descriptions for both collections and folders (where available)

Important: Folders share the same namespace as collections. Before creating a new collection, check the folders array to avoid naming conflicts (e.g., can't create a 'website' collection if a 'website' folder exists).

Sample Response:

{
	"collections": ["categories", "contacts", "organizations", "pages", "posts", "products"],
	"collection_folders": ["content", "marketing", "website"],
	"notes": {
		"contacts": "People at the organizations you work with",
		"organizations": "Your clients and customers",
		"pages": "Static pages with page builder blocks",
		"posts": "Blog posts and articles",
		"content": "Content management folder"
	}
}

Use case: Initial exploration, getting oriented with available data structures

Detailed Mode

Usage: Specify collections to examine

{ "keys": ["products", "categories", "users"] }

Returns: Object of collections with field and relation details

• Field definitions (type, validation, defaults)
• Relationship mappings (foreign keys, junction tables)
• Interface configurations and display options
• Field metadata and constraints
• Nested field structures for JSON fields with repeaters/lists (includes recursive nesting)

Sample Response:

{
	"posts": {
		"id": {
			"type": "uuid",
			"primary_key": true,
			"readonly": true
		},
		"title": {
			"type": "string",
			"required": true
		},
		"status": {
			"type": "string",
			"interface": {
				"type": "select-dropdown",
				"choices": ["draft", "published", "archived"]
			}
		},
		"category": {
			"type": "uuid",
			"relation": {
				"type": "m2o",
				"related_collections": ["categories"]
			}
		}
	},
	"block_faqs": {
		"headline": {
			"type": "text",
			"interface": {
				"type": "input-rich-text-html"
			}
		},
		"faqs": {
			"type": "json",
			"interface": {
				"type": "list"
			},
			"fields": {
				"title": {
					"type": "text",
					"interface": {
						"type": "input-multiline"
					}
				},
				"answer": {
					"type": "text",
					"interface": {
						"type": "input-multiline"
					}
				}
			}
		}
	}
}

Use case: Deep-dive analysis of specific collections before working with data or schema.

Recommended Workflow

1. Discover: Call without keys to see all available collections
2. Analyze: Based on user requirements, identify relevant collections
3. Detail: Call with specific collection names to understand field structures
4. Implement: Use appropriate CRUD tools with schema knowledge