best_model_for
Get an editorial shortlist for a known use case with verified constraints.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| use_case | Yes | ||
| constraints | No |
Get an editorial shortlist for a known use case with verified constraints.
| Name | Required | Description | Default |
|---|---|---|---|
| use_case | Yes | ||
| constraints | No |
Changes observed during successful MCP inspections.
Input schema / properties / use_case / enumPrevious value: -[
- "ai-influencer",
- "ai-video-generator",
- "character-consistency",
- "cinematic-video",
- "image-to-video",
- "product-videos",
- "social-shorts",
- "talking-characters",
- "text-in-images",
- "ugc-ads"
-]New value: +[
+ "ai-influencer",
+ "ai-video-generator",
+ "character-consistency",
+ "cinematic-video",
+ "image-to-video",
+ "product-shots",
+ "product-videos",
+ "realistic-humans",
+ "social-shorts",
+ "talking-characters",
+ "text-in-images",
+ "ugc-ads"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds modest value by implying the output is a curated/shortlist style rather than exhaustive, but says nothing about how constraints are validated or what 'verified' means operationally.
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 efficient sentence with the core action front-loaded and no filler. It is not padded, though its brevity comes at the cost of substance rather than from crisply conveying 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?
For a tool whose second parameter is an undocumented nested object and which has no output schema, the description leaves too much unsaid. An agent cannot confidently construct constraints or predict the shortlist's shape from this text alone.
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?
With 0% schema description coverage and no property definitions for the nested 'constraints' object, the description needed to compensate and does not. It alludes to 'verified constraints' but never explains what keys or formats the nested object accepts; only the self-descriptive enum values for use_case carry meaning.
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 names a verb ('Get') and a resource ('editorial shortlist') tied to a use case, so the general intent is discernible. However, 'editorial shortlist' and 'verified constraints' are vague jargon, and nothing distinguishes it from the overlapping siblings find_model, list_models, or compare_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 phrase 'for a known use case' weakly implies you should already have a use case in mind, but there is no explicit when-to-use guidance, no exclusions, and no named alternative (e.g., vs find_model for free-text search). An agent must guess which sibling to pick.
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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