370.ai — AI Gateway: Video (Seedance 2.0, Wan, HappyHorse), Image, Speech + 100+ Chat Models

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

No annotations are provided, so the description bears the full burden. It tells the agent that the tool returns the model's text reply, describes the 'model' default as 'fast cheap default', and explains parameter effects. However, it does not disclose potential costs, rate limits, or any side effects beyond the reply. There is no contradiction with annotations (none exist), but transparency is only partial.

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 front-loaded with the core purpose, followed by concise guidance and a neatly tabulated Args list. No filler or redundant sentences; every line earns its place. The structure is highly readable and aids an agent's quick parsing.

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 chat tool with 5 parameters, the description covers purpose, usage scenarios, parameter semantics, and the return value ('Returns the model's text reply'). It also references list_models for the full model catalog, and the existence of an output schema reduces the need to detail return format. Missing error-case or rate-limit info, but the essentials are sufficiently covered.

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 schema has no parameter descriptions (0% coverage), so the description compensates well. It explains each argument: prompt is 'the user message / question', model is a 'model id' with empty meaning 'fast cheap default', system is 'optional system instruction', max_tokens is 'optional cap on output length', and temperature is '0..2'. This adds meaning beyond the bare schema types and defaults.

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 function: 'Ask any text/chat model on 370.ai ... and get its reply.' It explicitly names the resource (models on 370.ai) and the action (ask/get reply), and distinguishes it from sibling tools like generate_image or text_to_speech.

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 explicit use cases: 'when you want a specific or different model than your own', gives concrete examples (consult Claude while on GPT, cheaper model for bulk work), and points to list_models for the catalog. Lacks an explicit 'when not to use' but the alternative is implied through list_models.

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 must carry the burden. It discloses that the tool 'Returns the generated PNG image,' which is a behavioral trait. However, it does not mention other behaviors such as whether the operation is synchronous, any API key requirements, rate limits, or potential side effects. For a simple image generation tool, the return type and size options provide some transparency, but not a full picture.

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 concise and well-structured: a single-purpose first sentence, followed by a compact args list. Every sentence earns its place with no extraneous detail. The format is easy to scan and understand.

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?

Given the low complexity (2 params, 1 required) and absence of an output schema, the description covers the essentials: purpose, parameter semantics, and return format. It does not address edge cases or failures, but for a simple generation tool this is reasonable and not a critical gap.

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 schema description coverage is 0%, so the description fully compensates. It explains 'prompt' as 'description of the image' and adds the tip that 'English works best.' For 'size,' it enumerates the exact allowed values and their orientation meanings (square, portrait, landscape), which is not present in the schema. This significantly helps the agent select appropriate parameter values.

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 verb ('Generate') and resource ('an image from a text prompt'), which distinguishes it from sibling tools like generate_video and text_to_speech. The phrase 'picture/illustration/photo' further clarifies the scope.

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 explicitly states when to use the tool: 'Use whenever the user wants a picture/illustration/photo created.' This is clear positive guidance. It does not explicitly mention exclusions or alternatives, but the context of sibling tools implies that for non-image generation, other tools are appropriate.

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 burden of behavioral disclosure. It clearly discloses that the operation is asynchronous, returns a task_id, and requires polling. This is key behavioral context, though it doesn't mention error handling, rate limits, or 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 extremely concise: a single clear lead sentence and a compact parameter list. Every line adds value, and the async flow is front-loaded. No wasted words or redundant details.

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?

Given the output schema exists, the description doesn't need to detail return values. It covers the essential workflow: initiate task, receive task_id, poll status. The parameter semantics are also covered. It lacks notes on potential errors or auth, but for a simple asynchronous generation tool, it is reasonably 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 0%, so the description must add meaning. It enriches the schema by providing examples: duration as seconds with 'e.g. 5', and resolution as '480p / 720p / 1080p', which clarifies allowed values beyond the schema defaults.

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 'Start a text-to-video generation (async)' with a specific verb and resource. It distinguishes itself from siblings like generate_image and get_video_status by emphasizing the asynchronous task creation and return of a task_id.

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 explicitly says 'poll get_video_status until done', providing direct guidance on the follow-up tool to use. It implies the tool is for initiating video generation, but doesn't explicitly mention when to avoid using it or compare it to generate_image beyond the async nature.

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 that when status=succeeded, the result contains the video URL, which is a key behavioral trait. However, it does not describe other possible statuses (e.g., failed, processing) or any side effects, leaving gaps in transparency.

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 highly concise, consisting of two short sentences with no filler. It front-loads the main verb and resource, and every sentence adds value. This is an example of efficient specification.

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?

Given the tool's simplicity (one parameter) and the existence of an output schema, the description is nearly complete. It explains the most important outcome (success yields URL). It could mention failure states, but the output schema likely covers return value structure. Overall, it is sufficient for a straightforward status-checking tool.

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 description does not mention the task_id parameter at all. With schema coverage at 0%, it fails to compensate by explaining that the task_id is likely returned from generate_video. The schema only provides a title 'Task Id', which is minimal. The description should have clarified the parameter's provenance.

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 function: 'Check a video generation task' with a specific verb and resource. It distinguishes itself from sibling tool generate_video by focusing on status checking rather than creation. The mention of 'status=succeeded' adds specificity.

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 the tool is used after initiating a video generation task (likely via generate_video) but does not explicitly say when to use it or provide alternatives. It lacks exclusionary guidance, but the purpose is clear enough that an agent can infer the usage context.

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 burden. It discloses that the operation is free and has no billing, which is a useful behavioral trait. As a simple list operation, no side effects is implied, adding some 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 a single, well-structured sentence that conveys purpose, scope, and cost upfront. Every word earns its place 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?

Given the tool's simplicity (0 params, output schema present), the description fully covers the necessary context. It explains what the tool does, its scope, and its cost, while the output schema handles return values.

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 tool has zero parameters, so the baseline is 4. The description adds no parameter details, which is appropriate since there are none to explain.

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 a specific verb ('List') and clearly identifies the resource ('AI models available on 370.ai'), further specifying categories (chat/image/video/audio). This distinguishes it strongly from sibling generation tools like chat and generate_image.

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 the tool (to see available models) by listing categories and noting it's free. It does not explicitly mention alternatives or exclusions, but the sibling context makes the use case clear.

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 must carry the full behavioral disclosure. It discloses that it returns a status with byte size, which is useful, but lacks details on potential limitations, audio duration caps, or rate limits. The description is adequate but not comprehensive.

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 sentence that is direct and free of unnecessary words. It front-loads the core function and provides concise return information.

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 only two parameters, but the description leaves the voice parameter undocumented and provides no additional context on output details beyond byte size. An output schema exists, but without seeing it, the description alone 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 0% and the description does not compensate. It mentions 'text' implicitly but adds no explanation of the 'voice' parameter or available voice options. The default 'alloy' is in the schema, but its meaning and usage are unexplained.

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: converting text to spoken audio in mp3 format. It also distinguishes itself from sibling tools like transcribe_audio by the verb 'Convert' and the output format specification.

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 (when needing audio from text) but does not explicitly state when not to use or mention alternatives like transcribe_audio for the reverse direction. This leaves usage guidance 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 of disclosure. It only states input and output, omitting any mention of side effects, failure modes, or requirements beyond public URL. For example, it does not disclose audio format limits, processing time, or error handling, leaving significant behavioral ambiguity.

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 9-word sentence that front-loads the verb 'Transcribe' and includes the key constraints (public URL, output text). Every word earns its place with no redundant or filler content.

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 an output schema (per context signal), so the description need not detail return values. However, it lacks operational context such as supported audio formats, language coverage, or whether the operation is synchronous. Given the low complexity, a few more details would make it 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?

The schema has only one parameter with zero description coverage, so the description partially compensates by adding that the audio must be from a public URL and that it transcribes spoken audio, which clarifies the parameter's meaning. However, it omits details like supported file formats or size limits, so it is not fully explicit.

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 'transcribe' and identifies the resource as 'spoken audio from a public URL', clearly distinguishing it from sibling tools like generate_image or text_to_speech. It also states the output is text, making the tool's purpose 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 provides clear context by specifying the input must be from a public URL, which indicates when this tool is applicable. It does not explicitly name alternatives or exclusions, but the sibling tools are distinct enough that no alternative transcription tool exists, so the context is sufficient.

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