Video Generation Cost Estimator

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.

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It openly details that estimates are sorted cheapest-first, include provenance and freshness, normalize credit-economy providers to USD, and assume stitched generations for clip-length limits. This is rich behavioral context beyond the schema.

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 three sentences: purpose and providers, output characteristics, and special handling. Every sentence adds substantive value with no redundancy or filler. It is front-loaded with the core purpose and then elaborates on behavior.

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?

Despite no output schema, the description explains the return envelope: estimates sorted, with source_url, verified_at, and confidence level. It also covers normalization and stitching behavior. For a tool with 5 parameters and no annotations, this description is sufficiently complete for an agent to select and invoke it correctly.

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?

Input schema coverage is 100%, so the baseline is 3. The description does not add much parameter-specific detail beyond what the schema already provides, but it does mention clip-length limits indirectly, which relates to duration_seconds. The schema already handles parameter semantics, so no compensation is needed.

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's purpose: estimating USD cost of generating AI video across multiple named providers. It specifies the output format (sorted cheapest-first with provenance, freshness, and confidence), which distinguishes it from sibling tools like compare_providers and get_pricing_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 this tool is for cost estimation but does not explicitly state when to use it vs compare_providers or get_pricing_changes. There is no mention of exclusions or alternative scenarios, so usage guidance is only implied through the tool's listed features.

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

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

No annotations are provided, so the description carries the full burden. It discloses the append-only nature (implying read-only), the default time window, and the types of events included. It doesn't cover response format or error behavior, but for a simple retrieval tool it offers solid context.

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 concise sentences, front-loaded with the primary purpose and key details. Every sentence adds useful information without redundancy.

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 single-parameter read tool, the description adequately covers purpose, usage, and default behavior. It doesn't explain the response shape, but no output schema exists and the term 'change log' implies a list. Sibling differentiation is clear.

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?

The only parameter 'since' has 100% schema description coverage, so the baseline is 3. The description adds value by stating that omitting the parameter defaults to the last 30 days, which clarifies the semantics beyond the schema's 'at or after this time.'

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 the specific verb 'Retrieve' and clearly identifies the resource as an 'append-only change log of pricing updates, model deprecations, and verification events.' This precisely distinguishes it from siblings like compare_providers and estimate_video_cost, which serve different purposes.

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?

It states 'Useful for detecting price changes since a previous check,' giving a clear use case, and notes the default 30-day window. However, it does not explicitly mention when not to use this tool or name alternatives, so it falls short of a 5.

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