Documentation Index
Fetch the complete documentation index at: https://magica.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
The workflow builder tools let AI assistants create, edit, and restructure workflows step-by-step. Each tool validates independently, so errors are caught early — your assistant gets a clear error and can self-correct.
Typical build flow
Discover nodes
Call list_node_types to see available node types and their ports.
Scaffold a workflow
Call create_workflow to create a workflow with Request and Response nodes.
Add processing nodes
Call add_node for each model (image generator, LLM, etc.).
Wire it together
Call connect_nodes to wire outputs to inputs — type-checked automatically.
Editing an existing workflow
Already built a workflow and need to tweak it? Use these tools:
update_node — change input values on an existing nodedelete_node — remove a node; all connected edges are cleaned up automaticallydisconnect_nodes — remove a single edge (by edge ID or source/target pair)add_node + connect_nodes — rewire the workflow
Examples
Four common patterns. Switch tabs to see each one.
Image + Describe
Parallel LLMs + Image
System Template
Direct Generation
Generate an image with Flux, then describe it with a vision LLM.list_node_types(category: "image") → sees flux_2_pro with out:result (image)
list_node_types(category: "llm") → sees gpt_5_4 with in:image_urls, out:output
create_workflow("Image + Describe") → gets workflowId + request/response node IDs
add_node(workflowId, "flux_2_pro") → gets flux node ID + ports
add_node(workflowId, "gpt_5_4") → gets LLM node ID + ports
connect_nodes(flux out:result → llm in:image_urls) → validated (image→image)
connect_nodes(llm out:output → response in:result) → validated (text→any)
Run two LLMs in parallel and feed both into an image generator.create_workflow("Parallel LLMs", requestFields: [
{name: "Cat", type: "text", value: "Cat"},
{name: "Dog", type: "text", value: "Dog"}
])
add_node(workflowId, "gpt_5_4_mini", column:1, row:0,
inputs: {system_prompt: "Provide me in 1 line."}) → LLM 1 (top)
add_node(workflowId, "claude_sonnet_4_6", column:1, row:1,
inputs: {system_prompt: "Provide me in 1 line."}) → LLM 2 (bottom)
add_node(workflowId, "nano_banana_pro", column:2, row:0) → image gen
connect_nodes(request field_cat → llm1 in:prompt)
connect_nodes(request field_dog → llm2 in:prompt)
connect_nodes(llm1 out:output → nano in:prompt)
connect_nodes(nano out:result → response result)
connect_nodes(llm1 out:output → response result)
connect_nodes(llm2 out:output → response result)
Run a saved system-workflow template by name — no building needed.start_run(workflowName: "Nano Banana Pro", values: {
prompt: "Add a dog to the scene",
image_url: "https://example.com/photo.jpg"
})
→ Fuzzy-matches user workflows first, then system templates.
→ Returns a runId. Use get_run_status(runId) to long-poll for the result.
start_run validates Request Node fields automatically. If you call it without values, it returns the field schema instead — call again with values filled in. Quick single-model task — no workflow, no template.search_tools(query: "image") → sees flux_2_pro, nano_banana_pro, etc.
get_model_schema(modelId: "flux_2_pro") → input field schema
execute_tool({
tool_name: "generate",
input: { modelId: "flux_2_pro", prompt: "A red Ferrari" }
}) → returns runId
get_run_status(runId) → long-polls until COMPLETED
Port names returned by add_node and list_node_types use the in: / out:
prefix format expected by connect_nodes. The connect_nodes tool validates
type compatibility (e.g. image→image), prevents cycles, and enforces
single-input rules. Use disconnect_nodes to remove a connection, or
delete_node to remove a node along with all its connections.
