Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it excels: it discloses async dispatch to AI workers (Modal), per-run credit costs that vary, credit exclusions (Day Pass/welcome credits), post-processing file deletion, auditability, and workspace entitlement rules. This is far richer than typical tool descriptions.

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 front-loaded with the core purpose and then efficiently packs operational facts (dispatch, credits, exclusions, deletion, audit, pricing). All sentences contribute useful information, though the workspace-subscription clarification is slightly tangential to tool invocation.

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 complexity (paid AI-worker dispatch, data retention, credit restrictions) and the absence of annotations and output schema, the description covers nearly all safety-relevant facts: cost, credit eligibility, file deletion, auditability, and plans. Minor gaps remain: it does not describe the result/return value or expected job duration, but with zero parameters the invocation surface is minimal.

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?

There are zero parameters and 100% schema coverage, so the baseline is 4. The description adds context around the implicit input ('text descriptions') and hints that 'model and file size' are cost determinants, though it does not define a formal input contract since none exists.

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 opens with a specific verb+resource ('Generate video from text descriptions using AI'), making the tool's purpose immediately clear. Among the many sibling AI tools, it uniquely and unambiguously identifies this as the text-to-video generator.

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?

Usage is implied through the purpose statement ('Generate video from text descriptions'), and the AI Studio/credits context adds operational constraints on when it can be used (requires paid credits). However, there are no explicit exclusions or comparisons with alternative video tools (e.g., clip_maker or video editors), so the agent gets only implicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.