Cancel a currently running flow. Sends a stop signal — the flow will stop after completing the current processing step. Use get_flow(view="status") to confirm it has stopped.
| Name | Required | Description | Default |
|---|---|---|---|
| flow_id | Yes | The unique identifier of the running flow to cancel. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive and idempotent behavior, but the description adds crucial context: the cancellation is not immediate; it waits for the current processing step to complete. This elaborates on the semantics beyond what annotations convey, without contradicting any annotation.
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 long, front-loaded with the core action, and contains no filler. Every sentence contributes: the first states the action and behavior; the second provides a verification step. This is a model of conciseness.
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 single-parameter, no-output-schema tool, the description is nearly complete. It covers what the tool does, how it behaves (graceful stop), and how to verify the result. The only minor gap is that it doesn't mention what happens if the flow is already stopped or if the flow_id is invalid, but annotations (idempotentHint) cover some of that.
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 already describes flow_id clearly as 'The unique identifier of the running flow to cancel.' Schema coverage is 100%, so the description adds little beyond the schema. The reference to get_flow(view="status") hints that flow_id is the same identifier used in status checks, but that's an implicit connection.
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 ('Cancel') and names the resource ('a currently running flow'), making the purpose unmistakable. It also distinguishes this from sibling tools like run_flow or delete_flow by specifying it targets running flows and sends a stop signal.
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 explicitly instructs how to confirm the cancellation using get_flow(view="status"), providing clear next-step guidance. It implies this tool is for running flows, but doesn't explicitly state when not to use it or contrast with delete_flow, which could be a valid alternative for removing flows entirely.
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.