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?

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?

With no annotations provided, the description carries full behavioral burden. It goes beyond a generic statement by exposing a critical quirk: the default model ignores OpenAI voice names and normalizes all six to the same audio, while also explaining how to achieve a real voice change via upstream names. This is exactly the kind of non-obvious behavior an agent needs to know before invoking the tool.

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 front-loaded with the core purpose. The parameter notes are structured and the extended voice warning is dense but necessary. It is slightly verbose due to the inline warning and code-like formatting, but every part 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 two-parameter tool with an output schema present, the description covers the core invocation details: what to pass, what unusual voice behavior to expect, and what kind of result is returned. It does not mention audio format, delivery mechanism, or text length constraints, but the presence of an output schema lowers the burden for return-value details.

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 explain the parameters, and it does: 'text' is defined as the text to speak, and 'voice' receives a detailed warning about name normalization and examples of valid upstream names. It adds substantial meaning beyond the bare schema, although it does not enumerate all possible voice values or text length limits.

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 precise verb and resource, 'Convert text to spoken audio', which fully defines the tool's function. It also distinguishes it from its siblings (chat, image/video generation, status, list_models) without needing to name them. The added detail about returning a status with byte size further clarifies observable behavior.

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 main sentence implies when to use this tool: whenever text needs to be turned into speech. It does not, however, explicitly state when not to use it or compare it to alternatives such as generate_video or chat. The usage context is inferable but not spelled out.

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