List the AI video or image models available on AI Content Drop with their flat credit cost per generation.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Model family to list. Defaults to video. | |
| max_credits | No | Only return models costing this many credits or fewer. |
| Name | Required | Description | Default |
|---|---|---|---|
| note | No | ||
| type | Yes | ||
| count | Yes | Models returned after filtering. | |
| models | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds 'flat credit cost per generation' as an output detail, but does not disclose additional behavioral traits such as default param values, pagination, or rate limits. 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?
A single, front-loaded sentence without filler. It names the action, resource, scope, and key output attribute in an efficient manner.
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 listing tool with two optional parameters, a full output schema, and strong safety annotations, the description is adequate. The only implicit detail, the default type of 'video', is already documented in the schema, so nothing critical is missing.
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 both parameters fully described in the input schema. The description reinforces the type enum ('video or image') and mentions cost, which relates to max_credits, but adds no new semantic meaning 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?
States a specific verb and resource: 'List the AI video or image models available on AI Content Drop with their flat credit cost per generation.' This clearly differentiates from siblings like list_generations (lists generations) and estimate_credit_cost (estimates cost for a specific generation).
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 clearly implies when to use the tool: when the agent needs to know which models are available and their flat credit costs. It does not explicitly mention alternatives or when-not-to-use, but the purpose is self-evidentifying and distinguishes from the other listing and estimation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
