Video-gen model catalog as lean TOON text. search filters by name, slug, description, or tags. Run video_plan first — plan resolves slug to validate here.
| Name | Required | Description | Default |
|---|---|---|---|
| search | No | ||
| onlyRecommended | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and idempotent behavior, so the description correctly avoids redundancy. It adds value by specifying the output format (lean TOON text) and search semantics (filters by name, slug, description, tags). 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?
Two short sentences, front-loaded with purpose, and every clause adds meaningful information. No fluff or repetition, making it an excellent example of concise structuring.
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 read-only list tool, the description covers purpose, output format, search, and a prerequisite. However, it omits explanation of the onlyRecommended parameter and doesn't detail what the output text contains (e.g., fields like slug or ID). Adequate but with clear gaps.
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 descriptions are 0%, so the description must document parameters. It explains 'search' well (filters by specific fields), but 'onlyRecommended' is never mentioned, leaving a full parameter undocumented. This is a significant gap in parameter coverage.
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?
Description clearly states the tool lists video generation models as text (TOON) and mentions search filtering. It effectively distinguishes from sibling model list tools by specifying 'video-gen', though it doesn't explicitly name alternatives like magnific_images_models_list.
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?
Provides a concrete usage prerequisite ('Run video_plan first') and explains search functionality. However, it does not explicitly state when to use this tool versus other model list tools (e.g., upscale models) or any exclusions, leaving the usage context somewhat implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.