Duplicate an existing flow. Creates a deep copy with fresh state (no results). Use this to copy a flow and then modify it with update_flow, instead of recreating from scratch.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Name for the new copy. Defaults to 'Copy of <original name>'. | |
| flow_id | Yes | The unique identifier of the flow to duplicate. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate it is not read-only and not destructive, but the description adds crucial context: it creates a deep copy and resets results (fresh state, no results). This explains behavioral nuance beyond the structured annotations, though it doesn't mention permissions or the exact return payload.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action, then a helpful usage note. Every word earns its place with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity mutation tool with no output schema, the description covers the main purpose, the key behavior (deep copy, fresh state), and typical follow-up action. It lacks explicit return-value details, but those are not essential given the schema and sibling context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for both parameters, including a description of the name default. The description adds no additional parameter-level semantics beyond what the schema already provides, so it meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Duplicate') and resource ('existing flow'), and explicitly distinguishes the action from creating from scratch by noting it copies an existing flow. It also clarifies the result is a deep copy with fresh state, setting it apart from other copy-like tools such as fork_template.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance: use this to copy a flow then modify it with update_flow, instead of recreating from scratch. It names an alternative tool (update_flow) and implies contrast with create_flow, though it doesn't explicitly mention when not to use it for forking community flows or templates.
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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Each tool targets a distinct resource and action, with clear separation between flow lifecycle, execution, model exploration, community, and account tools. Even similar-sounding tools like create_flow, preview_flow, and suggest_flow have clearly different purposes (actually creating, dry-running, and recommending models). Descriptions prevent misselection.
All tools follow a consistent verb_noun pattern in snake_case (e.g., create_flow, list_flows, run_batch, cancel_flow). No mixed conventions or vague verbs like 'process' or 'handle'. The naming is uniform and predictable.
At 33 tools, this is a large surface, but each tool addresses a distinct feature of the cnaps.ai platform, from flow CRUD and execution to community features and notifications. Still, it exceeds the typical well-scoped range and feels heavy, making it a borderline case between appropriate and too many.
The core flow lifecycle (create, read, update, delete, restore, duplicate) and execution (run, batch, cancel) are covered, but structural editing of flow graphs is missing—update_flow only changes parameters, not topology. Additionally, there is no run history, batch list/cancel, or community post update/delete, leaving notable gaps for a platform API.