List curated flow templates on cnaps.ai. Templates are pre-built, tested flows for common tasks. Fork a template with fork_template to use it.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate these. It adds context about templates being 'pre-built, tested flows for common tasks' but provides no additional behavioral details such as pagination, sorting, or output format. This is acceptable given the annotations but lacks extra insight beyond them.
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 exactly two sentences. The first states the purpose, and the second provides useful context about templates and points to the next action (fork_template). Every word earns its place, with no redundancy or fluff.
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 simple zero-param, read-only list tool, the description is nearly sufficient. It explains what templates are and how to proceed after listing. However, since there is no output schema, it could briefly mention what the response returns (e.g., a list of template summaries), but this is a minor gap rather than a critical omission.
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 tool has zero parameters, and the schema coverage is 100% (trivially). The description needs no parameter explanation and correctly implies no inputs are required. The baseline for 0 params is 4, which is appropriate here.
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 'List curated flow templates on cnaps.ai' with a specific verb (list) and resource (curated flow templates). It distinguishes from siblings like list_flows (which lists user flows) and fork_template (which forks), leaving no ambiguity about its function.
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 conveys clear context for when to use this tool—to browse curated templates. It also provides a direct follow-up action ('Fork a template with fork_template to use it'), indicating a usage workflow. However, it does not explicitly contrast with alternatives (e.g., list_flows or search_community), so it stops short of a full 5.
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.