Estimate the credit cost of generating with a specific model, optionally for a batch of several generations.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | Model ID, e.g. "kling_3_0" or "veo_3_fast". | |
| quantity | No | How many generations. Defaults to 1. |
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| type | Yes | ||
| model_id | Yes | Normalized to underscores. | |
| quantity | Yes | ||
| credits_each | Yes | ||
| credits_total | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the batch-scoping behavior but does not explicitly clarify that no actual generation occurs; that must be inferred from the word 'estimate'. No contradiction with annotations.
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 a single sentence that immediately states the core action and scope, then adds the optional batch condition. Every word earns its place, with no redundant or irrelevant detail.
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, full parameter coverage, present output schema, and read-only annotations, the description is sufficient. It communicates the purpose, the required model_id, and the optional quantity semantics that an agent needs to invoke the tool correctly.
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 description coverage is 100%, so both parameters are already well-documented. The description adds modest semantic context by tying 'specific model' to model_id and 'batch of several generations' to quantity, but it does not provide additional syntax, constraints, or examples beyond the schema.
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 ('estimate') and a clear resource ('credit cost'), scoped to a specific model and optionally a batch. This clearly distinguishes it from sibling tools like generate_video, which actually create content, and list_models, which simply enumerates models.
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 intended use case is implied: call this when you need to know the credit cost before generating with a model. However, the description does not explicitly state when to prefer this over alternatives, nor does it mention that it should be used before generate_video or that it does not trigger generation.
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 clearly distinct resource or action: account balance, article retrieval/search, model listing, cost estimation, generation triggering, and generation status/list. Even the two cost-related tools (list_models and estimate_credit_cost) are distinguishable, since one lists catalog prices and the other estimates a specific batch.
All tool names follow the same lowercase snake_case verb_noun pattern: generate_, get_, list_, search_, estimate_. The verbs are consistent and predictable, making it easy to guess the tool name for a desired action.
At 8 tools, the set is well-scoped for a platform covering video generation, account management, and a supporting article/knowledge base. Each tool earns its place and the count feels neither sparse nor bloated.
The core generation lifecycle is covered: estimate cost, generate, poll status, list history, and check account balance. Article search and retrieval are also covered, but the platform appears to offer image models based on list_models while only video generation is exposed, leaving an obvious generation gap.
