Run a flow in batch mode with multiple input file sets. Each input node receives an array of file URLs (uploaded via upload_file). Returns a batch_run_id to track progress.
| Name | Required | Description | Default |
|---|---|---|---|
| inputs | Yes | JSON object mapping input node IDs (as strings) to arrays of file URLs. Example: {"12": ["https://...url1", "https://...url2"]} | |
| flow_id | Yes | The flow to run in batch mode. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a non-read-only, non-destructive, non-idempotent operation. The description adds that input files must be uploaded via upload_file and that a batch_run_id is returned for tracking. It does not contradict annotations and adds useful context beyond the structured data.
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-loading the core purpose and then adding relevant details. Every sentence contributes value without 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?
The description covers purpose, input format, prerequisite (upload_file), and return value (batch_run_id). It implies progress tracking but does not explicitly mention checking status via get_batch_status. For a tool with no output schema, this is fairly complete, though it could hint at async status checking.
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 is 100% with detailed descriptions for both parameters, including an example for inputs. The description reinforces the file URL array concept but does not add significant new meaning beyond the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 the tool runs a flow in batch mode with multiple input file sets, distinguishing it from the sibling run_flow which presumably runs single sets. The verb 'Run' and resource 'flow in batch mode' are specific and unambiguous.
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 context for use: batch mode with multiple input file sets, implying it is for batch processing. It does not explicitly name run_flow as the alternative for single runs, nor does it state when not to use, but the context strongly implies the appropriate scenario.
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.