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create_fusion_node_chain

Builds a sequential chain of Fusion nodes in the current composition, connecting each node's input to the previous output. Define node types, names, and parameters to automate graph creation.

Instructions

Create a chain of connected Fusion nodes in the current composition.

Creates multiple nodes and connects them sequentially (each node's Input is connected to the previous node's output). This is useful for building Fusion node graphs programmatically.

Each node dict supports:

  • "type" (required): The Fusion node type (e.g. "Background", "Blur")

  • "name" (optional): Custom display name for the node

  • "params" (optional): Dict of parameter name-value pairs

Args: nodes: List of node definitions. Each is a dict with "type" (required), optional "name", and optional "params".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must disclose behavior. It explains that nodes are created and connected sequentially in the current composition, but does not cover error conditions, empty input, or permissions. Adequate but not exhaustive.

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 concise, uses a clear structure (main sentence, explanation, uses, then parameter details). Every sentence adds value without redundancy.

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?

The description covers the parameter structure and overall purpose. Given that an output schema exists, return values are not needed. However, it could mention potential errors or the need for an open composition.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema only defines 'nodes' as an array of objects with 'additionalProperties: true', which is vague. The description adds essential semantics: each dict requires 'type', and optionally accepts 'name' and 'params'. This significantly enriches the 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 action ('Create') and the resource ('chain of connected Fusion nodes'). It distinguishes from the sibling 'create_fusion_node' by specifying sequential connections, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context by stating it is 'useful for building Fusion node graphs programmatically.' It implies appropriate use cases but does not explicitly exclude alternatives like using 'create_fusion_node' for single nodes.

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

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