Restore a flow from trash back to active state. Use list_flows with state='trashed' to find trashed flows.
| Name | Required | Description | Default |
|---|---|---|---|
| flow_id | Yes | The unique identifier of the trashed flow to restore. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide key safety signals (readOnlyHint=false, idempotentHint=true, destructiveHint=false). The description adds the specific state transition ('from trash back to active state') but does not disclose additional behavioral details such as permissions, side effects, or edge cases. Given the annotations, this is adequate but not rich.
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?
The description is two sentences, front-loaded with the purpose, and includes a helpful tip for finding trashed flows. Every word earns its place; no fluff or redundancy.
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?
Given the tool's simplicity (one parameter, no output schema), the description is complete enough. It tells the user what the tool does and how to locate the right input. The annotations and schema cover the remaining details, so no further explanation is needed.
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?
Schema coverage for the single parameter (flow_id) is 100%, with a clear description in the schema. The tool description does not add extra meaning or clarify syntax beyond what the schema already provides, so the baseline of 3 is appropriate.
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 clearly states a specific action (restore) on a specific resource (a trashed flow) and the result (back to active state). It distinguishes restore_flow from siblings like delete_flow or cancel_flow by focusing on the trash-to-active transition.
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 gives a clear when-to-use context: use when you need to restore a trashed flow. It also provides a practical prerequisite via list_flows with state='trashed'. However, it does not explicitly mention alternatives or when not to use the tool, so it stops short of full exclusion guidance.
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.