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analyze_workflow

Analyze any saved ComfyUI workflow to understand its sections, nodes, connections, and data flow. Get a structured summary for AI reasoning before modifying or executing.

Instructions

Load a saved workflow and return a structured analysis — sections, node settings, connections, and data flow. Use this to understand any workflow before modifying or executing it. Returns a concise text summary (not raw JSON) optimized for AI reasoning. Prefer this over get_workflow unless you need the raw JSON for enqueue_workflow or modify_workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYesWorkflow filename (e.g. 'Scene Builder v3.json'). Use list_workflows to see available files.
viewNosummary (default): structured text with sections, node IDs, key settings, virtual wires, and full connection graph — best for AI understanding. overview: mermaid diagram showing sections as summary nodes with cross-section data flow. detail: mermaid diagram for one section (requires section parameter). list: text listing of all sections with data flow summary. flat: single mermaid flowchart of the entire workflow (best for small workflows).summary
sectionNoSection name for detail view. Use view='list' first to see available section names.
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses that the tool returns a concise text summary optimized for AI reasoning (not raw JSON), which is a key behavioral trait. No contradictions.

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?

Three sentences, each purposeful: purpose, usage guidance, output format and sibling differentiation. No fluff, front-loaded.

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

Completeness5/5

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

Despite no output schema, the description explains the return format (text summary) and the view options. With 3 well-documented parameters and a clear use case, the description is complete for an analysis tool.

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?

Schema already provides descriptions for all three parameters (100% coverage). Description adds extra context: for filename, it suggests using list_workflows; for view, it explains each enum option; for section, it advises using view='list' first. Significant added value.

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?

Clearly states the tool loads a saved workflow and returns a structured analysis including sections, node settings, connections, and data flow. It differentiates from sibling get_workflow by specifying when to use which.

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

Usage Guidelines5/5

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

Explicitly says to use this to understand workflows before modifying or executing, and advises preferring it over get_workflow unless raw JSON is needed for enqueue_workflow or modify_workflow. Also suggests using list_workflows to find filenames.

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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