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

chat

Ask any text/chat model on 370.ai (Claude / GPT / Gemini / DeepSeek / Qwen / ...) and get its reply.

Use this when you want a **specific or different** model than your own — e.g. consult Claude while you run on
GPT, use a cheaper model for bulk work, or reach a model you don't have direct access to. One key, 100+ models.
Call list_models to see the full catalog of model ids.

Args:
    prompt: the user message / question.
    model: model id to call (see list_models). Empty = a fast cheap default.
    system: optional system instruction to steer the model.
    max_tokens: optional cap on output length (omit to let the model decide).
    temperature: optional sampling temperature 0..2 (omit for the model's default).
Returns the model's text reply.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No
`prompt`	Yes
`system`	No
`max_tokens`	No
`temperature`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.2/5.0

Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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

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TDQS

A4.1/5.0

Disambiguation5/5

Each tool maps to a distinct modality or lifecycle step: chat, image generation, video generation, video status polling, model listing, and speech synthesis. The async video pair (generate_video/get_video_status) is clearly separated, and no two tools plausibly do the same thing.

Naming Consistency4/5

Most tools follow a clear verb_noun pattern: generate_image, generate_video, get_video_status, list_models. The deviations are minor: 'chat' is a bare verb and 'text_to_speech' uses a noun_phrase style instead of generate_speech or synthesize_speech, but the overall naming is still predictable.

Tool Count5/5

Six tools is well-scoped for a multimodal AI gateway covering chat, image, video, speech, and model discovery. Each tool earns its place and the count is comfortably within the ideal range.

Completeness4/5

The core workflows are covered: chat has a direct call, video generation has an async create-and-poll lifecycle, image and speech have generation entry points, and list_models aids discovery. Minor gaps exist—no image-to-video, speech status polling, or model selection for image/video/speech—but agents can accomplish the primary stated purposes.

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