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get_node_info

Retrieve configuration details for ComfyUI workflow nodes, including inputs, outputs, and functionality, to understand how to properly set up nodes in automation pipelines.

Instructions

Get detailed info about a node.

    Args:
        node_name: Node class name (e.g., 'RemoteCheckpointLoader_fal')

    Returns node information including:
    - input: Required and optional inputs with types
    - output: Output types
    - category: Node category
    - description: What the node does

    Use this to understand how to configure a node in a workflow.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
node_nameYesExact node class name

Implementation Reference

  • MCP tool handler for get_node_info. Fetches node details from ComfyUI /object_info endpoint, parses with NodeInfo model, handles errors.
    @mcp.tool()
    def get_node_info(
        node_name: str = Field(description="Exact node class name"),
        ctx: Context = None,
    ) -> dict:
        """Get detailed info about a node.
    
        Args:
            node_name: Node class name (e.g., 'RemoteCheckpointLoader_fal')
    
        Returns node information including:
        - input: Required and optional inputs with types
        - output: Output types
        - category: Node category
        - description: What the node does
    
        Use this to understand how to configure a node in a workflow.
        """
        if ctx:
            ctx.info(f"Fetching info for: {node_name}")
    
        try:
            result = comfy_get(f"/object_info/{node_name}")
            if node_name in result:
                data = result[node_name]
                data["name"] = node_name  # Ensure name is set
                info = NodeInfo(**data)
                return info.model_dump()
            return ErrorResponse.not_found(
                f"Node '{node_name}'",
                suggestion="Use list_nodes() to see available nodes",
            ).model_dump()
        except Exception as e:
            return ErrorResponse.unavailable(str(e)).model_dump()
  • Pydantic model defining the structure and validation for node information output by the tool.
    class NodeInfo(BaseModel):
        """Information about a ComfyUI node from /object_info."""
    
        name: str
        display_name: str | None = None
        description: str | None = None
        category: str | None = None
        input: NodeInput = Field(default_factory=NodeInput)
        output: list[str] = Field(default_factory=list)
        output_name: list[str] = Field(default_factory=list)
        output_node: bool = False
        deprecated: bool = False
        experimental: bool = False
  • Registration function that calls register_discovery_tools(mcp), which defines and registers the get_node_info tool.
    def register_all_tools(mcp):
        """Register all tools with the MCP server."""
        register_system_tools(mcp)
        register_discovery_tools(mcp)
        register_workflow_tools(mcp)
        register_execution_tools(mcp)
  • Helper function in API module that performs the core API call used by the tool handler.
    def get_node_info(node_name: str) -> dict:
        """Get info for a specific node."""
        return comfy_get(f"/object_info/{node_name}")
  • Pydantic model for node input schema used within NodeInfo.
    class NodeInput(BaseModel):
        """Node input specification."""
    
        required: dict[str, Any] = Field(default_factory=dict)
        optional: dict[str, Any] = Field(default_factory=dict)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns specific information (input, output, category, description), which is useful behavioral context. However, it doesn't mention error handling (e.g., what happens if the node doesn't exist), performance aspects (e.g., speed or caching), or authentication needs, leaving gaps in behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the main purpose in the first sentence. The bullet points efficiently list return values, and the final sentence provides usage context. There's minimal waste, though the bullet points could be slightly more concise, and the structure is clear but not perfectly optimized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (simple read operation with one parameter) and lack of annotations and output schema, the description is somewhat complete but has gaps. It explains what information is returned, which helps, but doesn't cover error cases, response format details, or how it integrates with sibling tools like 'add_node' or 'update_node_input,' making it adequate but not fully comprehensive.

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?

The schema description coverage is 100%, so the schema already documents the parameter 'node_name' with a description. The description adds value by providing an example ('RemoteCheckpointLoader_fal') and clarifying that it's the 'Node class name,' which enhances understanding beyond the schema's 'Exact node class name.' This compensates well, though it doesn't detail format constraints beyond the example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed info about a node'), and it distinguishes this from siblings like 'list_nodes' (which likely lists nodes without details) or 'search_nodes' (which searches for nodes). However, it doesn't explicitly contrast with all siblings, such as 'get_history' or 'get_system_stats', which also retrieve information but about different resources.

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 by stating 'Use this to understand how to configure a node in a workflow,' which suggests it's for learning about node configuration. However, it doesn't explicitly state when to use this tool versus alternatives like 'list_nodes' (for overview) or 'search_nodes' (for finding nodes), and it lacks guidance on prerequisites or exclusions, such as whether the node must exist or be available.

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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