PromptFrenzy

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. It reveals auth and plan requirements, but does not state whether the original generation is modified, whether video creation is asynchronous, what the output artifact is, or any cost/rate implications. The word 'turn' implies transformation without clarifying side effects.

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 a single front-loaded sentence that states the core purpose first, then the key prerequisites. There is no filler, repetition, or unnecessary 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 simple two-parameter tool, the description plus schema covers the basic inputs and prerequisites. However, with no output schema and no annotations, it leaves out what the agent should expect as a return value, whether the operation is synchronous, and what failure modes may occur, so it is only minimally complete.

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 description coverage is 100%, so the schema already documents generation_id and motion. The description adds no new parameter-level meaning beyond reinforcing that the generation must be finished, which matches the schema's 'ready' wording.

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 and resource: 'Turn a finished PromptFrenzy image generation into a short video clip.' This clearly distinguishes it from sibling tools like generate, get_generation, and search_prompts, which handle creation or retrieval rather than animation.

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 provides clear invocation context by requiring a finished image generation and stating the user's API key and plan-level video support. It does not explicitly name alternatives or exclusion cases, but the prerequisite guidance makes when-to-use reasonably explicit.

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 the full behavioral burden and does so admirably. It discloses the mutation (starts a render), the side effect (spends user's credits), the async pattern (returns id immediately, poll later), and the auth prerequisite (needs API key) — all beyond what the schema conveys.

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 dense sentences with zero waste. The main action is front-loaded, followed by the async pattern and the credit/auth caveat. Every clause earns its place.

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 no-annotation, no-output-schema tool with a nested object, the description covers the essentials: invocation, async result retrieval, side effects, and auth. It lacks error-case handling (e.g., missing/invalid API key) and the animate distinction, but these are minor gaps against an otherwise strong definition.

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%, so all three parameters (image_url, prompt_id, text_variables) are fully documented in the schema. The description adds no parameter-level detail beyond what the schema already provides, so the baseline 3 applies.

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 states a specific verb and resource — 'Run a PromptFrenzy prompt on the user's account and start a render' — which clearly defines the tool's function. It also distinguishes itself from the get_generation sibling by noting it returns an id immediately and that polling is needed for results.

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 provides implicit usage guidance: it routes to get_generation for results and implies credit cost is a consideration. However, it doesn't explicitly say when NOT to use this tool or how it compares to animate (the closest sibling), leaving the selection logic partially to inference.

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 states the high-level behavior but does not disclose what happens before completion, whether the tool is safe to poll repeatedly, or what response states to expect. For a status-checking tool, this is a meaningful gap.

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?

One sentence that conveys both the purpose and the resource, with no filler. The key action and expected output are front-loaded and clearly stated.

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?

The tool is simple with one parameter and no output schema, so the description need not be lengthy. However, it omits the behavior when the generation is not yet finished and does not describe the return shape beyond mentioning the URL, leaving a moderate gap in what an agent needs to handle response states.

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%, and the schema already explains that generation_id is 'Id returned by generate or animate.' The description adds no further parameter-level detail beyond the schema, so the baseline score of 3 applies.

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 specific action ('check whether finished') and result ('get its image or video URL') on a specific resource ('PromptFrenzy generation'). It clearly differentiates this from sibling tools like generate, animate, and get_prompt.

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 makes the polling use case clear: call this after a generation has been started, using the id from generate or animate. It does not explicitly name alternatives or exclusions, but the context is strong enough for an agent to know when it applies.

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, the description carries the full burden. It discloses a key behavioral trait: the prompt TEXT is only returned when an API key is supplied. This is critical for an agent to know before calling, as it affects the outcome. It also clarifies what the tool does NOT return (the full text without a key). It doesn't mention error cases or rate limits, but the disclosed conditional behavior is significant and goes beyond a simple 'get' description.

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 sentences, front-loaded with the core purpose and return attributes, followed by the critical conditional about the API key. Every word earns its place; no fluff or repetition of the schema.

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 single-parameter read tool with no output schema, the description covers the essential return values and the key behavioral condition (API key requirement for prompt text). It doesn't describe the exact response format, but with no output schema and a simple metadata read, the description is nearly complete. The only minor gap is not specifying what happens if the id is invalid or the prompt doesn't exist.

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% and the only parameter (id) is described as 'Prompt id from search_prompts.' The description reinforces this by implying the id identifies a specific prompt. It adds a bit of context by explaining what the tool returns for that id, but the schema already covers the parameter's meaning. Baseline 3, with a slight bump because the description clarifies the id's provenance (from search_prompts) and ties it to the tool's purpose.

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 reads a single PromptFrenzy prompt and enumerates the specific attributes returned (what it makes, model, cost, inputs). It distinguishes itself from siblings like search_prompts (which finds prompts) and get_generation (which likely retrieves a generation result) by focusing on reading one prompt's metadata. The verb 'Read' is specific and the resource is unambiguous.

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 when to use this tool: when you need details about a specific prompt, and the id parameter references search_prompts, indicating a workflow. It doesn't explicitly state when not to use it or name alternatives, but the sibling context and the reference to search_prompts provide clear context. A small gap: it doesn't explicitly say 'use search_prompts to find the id first' as a directive.

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 the full burden. It discloses return contents (titles, input requirements, credit cost, page URL) and the authentication requirement (no account needed). It does not mention rate limits or error behavior, but for a simple search operation these are minor omissions.

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 compact and well-structured: it opens with the core purpose, lists supported categories, then states the return value and access requirement. Every sentence adds value with no 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 search tool with fully documented parameters and a clear return description, the description is nearly complete. It lacks explicit differentiation from sibling tools, but the verb 'search' alone provides sufficient context. Minor gaps like result ordering or search behavior are not critical.

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 description coverage is 100%, so the schema already explains both parameters. The description adds only a small nuance ('in their own words' for query) which slightly clarifies user intent but does not significantly enhance understanding beyond the schema. Baseline 3 applies.

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 a specific action (Search) on a specific resource (PromptFrenzy library) and enumerates the types of prompts it covers. It is unambiguous and easily distinguished from sibling tools like generate or animate, which imply creation rather than retrieval.

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 (search to find prompts) and mentions 'No account needed' as a precondition, but it does not explicitly state when to use this tool versus siblings or when not to use it. There is no mention of alternatives, leaving the agent to infer the appropriate context.

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