Search AI Content Drop's published guides and model comparisons about AI video generation, prompting, and ad creative.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum results (1-25). Defaults to 10. | |
| query | Yes | Keywords, e.g. 'kling vs sora' or 'ugc hooks'. |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | ||
| query | Yes | ||
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile with readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds content scope but no additional behavioral traits such as pagination, rate limits, or result ordering, so it stays at a baseline 3.
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 front-loaded sentence with no filler. It packs the verb, resource scope, and topic coverage efficiently, making it easy for an agent to parse quickly.
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 search tool with two fully documented parameters, safety annotations, and an output schema, the description is largely sufficient. It is slightly incomplete in that it doesn't hint at the recommended flow of searching then fetching an article via get_article, but nothing essential to invoking the tool correctly 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 description coverage is 100%: query and limit are both documented with examples and constraints. The description adds no parameter-specific meaning beyond what the schema already provides, so the baseline 3 is appropriate.
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 and resource: 'Search AI Content Drop's published guides and model comparisons' about AI video generation, prompting, and ad creative. It clearly differentiates from generation and listing tools, though it does not explicitly distinguish itself from the closely related get_article sibling.
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 implies usage when discovering published content by keyword, such as 'kling vs sora', but it provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives like get_article for retrieving a specific article by ID.
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.
