compare_providersList raw, provenance-backed pricing rows for AI video generation providers and models. Every row carries source_url and verified_at. Optionally filter by provider slug (e.g. "fal", "google", "kling", "luma", "runway", "higgsfield") or model slug.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Model slug to filter by | |
| provider | No | Provider slug to filter by |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It reveals that every row includes source_url and verified_at, indicating provenance and freshness. It does not mention pagination, sorting, or whether the list includes all providers, but the 'raw, provenance-backed' phrasing adds useful context beyond the name.
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 two sentences, front-loaded with the main action and resource, and includes examples compactly without redundancy. Every sentence contributes to understanding purpose, filters, and output characteristics.
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 list tool with two optional filters and no output schema, the description conveys the core return shape (rows with source_url and verified_at) and filtering options. It could mention the actual pricing fields or the expected output format, but the provided information is sufficient for most selection and invocation cases.
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%, but the description adds meaning by clarifying that both provider and model filters are optional and providing concrete provider slug examples like 'fal', 'google', and 'runway'. This goes beyond the schema's generic 'slug to filter by' descriptions, though it does not explain model slug format or matching behavior.
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 clearly states the tool 'List raw, provenance-backed pricing rows' for AI video generation providers and models, using a specific verb and resource. It distinguishes itself from siblings (estimate_video_cost, get_pricing_changes) by emphasizing raw rows with source_url and verified_at rather than estimates or changes.
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 context by positioning the tool as a raw list with optional filters, and gives provider slug examples. However, it does not explicitly state when to prefer this over alternatives like estimate_video_cost or get_pricing_changes, nor any exclusions.
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 serves a clearly distinct purpose: compare_providers returns raw pricing rows, estimate_video_cost provides computed cost estimates with assumptions, and get_pricing_changes tracks updates over time. There is minimal overlap, and descriptions make the separation obvious.
All tool names follow a consistent verb_noun pattern: compare_providers, estimate_video_cost, get_pricing_changes. The naming is predictable and clearly indicates the action and object.
Three tools is appropriate for the narrow, focused purpose of video generation cost estimation. Each tool fills a necessary role without redundancy or bloat.
The tool set covers the core needs of the domain: listing raw pricing data, computing cost estimates, and tracking changes over time. There are no obvious gaps for the stated purpose of cost estimation.