370.ai — AI Gateway: Video (Seedance 2.0, Wan, HappyHorse), Image, Speech + 100+ Chat Models
Server Details
One key, 100+ models — chat with any LLM and generate video, images, speech. Free trial at 370.ai.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.8/5 across 7 of 7 tools scored. Lowest: 2.9/5.
Each tool targets a distinct modality: chat for text models, generate_image for image, generate_video/get_video_status for video, list_models for catalog, text_to_speech for speech synthesis, transcribe_audio for speech recognition. No overlap.
Most tools follow a clear verb_noun pattern (generate_image, generate_video, get_video_status, list_models, transcribe_audio). 'chat' and 'text_to_speech' deviate slightly, but the naming is still predictable and readable.
7 tools is well-scoped for an AI gateway covering chat, image, video, and speech. Each tool earns its place with no redundancy.
The tool surface covers all advertised capabilities: chat models, image generation, video generation with status polling, model listing, text-to-speech, and speech-to-text. No obvious gaps for the intended domain.
Available Tools
7 toolschatAInspect
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.| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| prompt | Yes | ||
| system | No | ||
| max_tokens | No | ||
| temperature | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description covers basic behavior (returns text reply) and parameter effects, but does not mention rate limits, error handling for invalid models, or cost implications. Transparency is adequate but not thorough.
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 well-organized: a brief purpose statement, usage guidance paragraph, and a bullet-like Args list. It is not overly verbose, though the usage paragraph could be slightly more concise.
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 and the presence of an output schema, the description covers essential aspects. It explains parameters, usage context, and links to list_models. Some behavioral details (e.g., default model behavior) are noted, but could be more explicit.
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?
With 0% schema description coverage, the description fully explains each parameter: prompt (required user message), model (IDs, default empty for fast/cheap), system (optional system prompt), max_tokens (output cap), temperature (sampling range). This adds crucial meaning beyond the schema types.
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 that the tool sends prompts to various text/chat models on 370.ai and returns their replies. It lists specific models (Claude, GPT, etc.) and distinguishes from sibling tools like image/video generation.
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 explains when to use this tool (e.g., to consult a different model or use a cheaper model) and suggests listing models for IDs. It lacks explicit 'when not to use' but the context of sibling tools implies it's for text chat.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_imageAInspect
Generate an image from a text prompt. Use whenever the user wants a picture/illustration/photo created.
Args:
prompt: description of the image (English works best).
size: 1024x1024 (square) / 1024x1536 (portrait) / 1536x1024 (landscape).
Returns the generated PNG image.| Name | Required | Description | Default |
|---|---|---|---|
| size | No | 1024x1024 | |
| prompt | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover all behavioral aspects. It mentions return type (PNG) and parameter effects, but does not disclose potential limitations like rate limits, content moderation, or failure modes. Adequate but not thorough.
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?
Extremely concise: two lead sentences followed by parameter list. No redundant information. Front-loaded with purpose.
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?
Single-function tool with no output schema. Description covers return type and key parameters. Could mention prompt language best practices or size validation, but overall adequate.
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 0%, but description adds full meaning: prompt is English text, size has three specified resolutions with aspect ratio examples. Adds value beyond schema.
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?
Description clearly states 'Generate an image from a text prompt' with specific verb and resource. It explicitly lists use cases (picture/illustration/photo) and distinguishes from sibling tools like generate_video.
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 clear context: 'Use whenever the user wants a picture/illustration/photo created.' Lacks explicit when-not-to-use or comparison with alternatives, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_videoAInspect
Start a text-to-video generation (async). Returns a task_id; poll get_video_status until done.
Args:
prompt: description of the video.
duration: seconds (e.g. 5).
resolution: 480p / 720p / 1080p.| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| duration | No | ||
| resolution | No | 720p |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description discloses async behavior and polling requirement but lacks details on rate limits, costs, or typical generation time.
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, front-loaded with the main purpose, and includes clear parameter explanations with no wasted words.
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 and the presence of an output schema, the description adequately covers the async nature and return value, but could mention error handling or limits.
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?
With 0% schema coverage, the description adds meaningful context for all three parameters (e.g., duration in seconds, resolution as specific values), though prompt could be more detailed.
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 it is an async text-to-video generation tool returning a task_id, distinguishing it from siblings like generate_image and get_video_status.
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?
Describes the async workflow and polling with get_video_status, but does not explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_video_statusCInspect
Check a video generation task. When status=succeeded the result contains the video url.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses that on success the result contains a video URL, indicating a polling mechanism. However, it does not explain other possible statuses, error states, or idempotency, leaving gaps.
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, efficient and without filler. It front-loads the purpose and key condition. However, the structure could be improved by separating the condition into a clearer statement.
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 lack of annotations and the presence of an output schema (content unknown), the description does not fully explain the tool's behavior. It omits details about other statuses, retry logic, or how to interpret non-success responses, which is important for a 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 only parameter, task_id, has no description in the schema (0% coverage). The description adds that it refers to a video generation task, but does not clarify how to obtain it (e.g., from generate_video response) or format constraints.
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 checks a video generation task and reveals the condition for getting a video URL, which is a specific verb and resource. It distinguishes from sibling tools like generate_video (creation) and list_models (listing).
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?
No explicit guidance on when to use this tool versus alternatives. It implies it is used after initiating a video generation task, but does not state prerequisites, polling behavior, or when not to use it (e.g., for other status checks).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_modelsAInspect
List the AI models available on 370.ai (chat / image / video / audio). Free, no billing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It declares the tool is free with no billing, but lacks details on side effects (none expected), rate limits, or authentication requirements. The behavioral profile is implied read-only but not explicit.
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: two short sentences providing essential purpose and cost information. No filler or 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 has zero parameters and an output schema (not shown but present), the description covers the purpose, platform, categories, and cost. It is complete for an agent to decide when to use this simple listing 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?
There are no parameters in the schema, so no additional meaning is needed. The description adds value by noting the tool is free and categorizes model types, which is contextually useful beyond the empty schema.
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 resource ('AI models') with clear categorization (chat/image/video/audio) and platform identifier ('370.ai'), distinguishing it from sibling tools like chat or 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 states 'Free, no billing' indicating no cost concern, implying it can be used freely. However, it does not explicitly state when to use this versus other tools, such as before invoking generation tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
text_to_speechBInspect
Convert text to spoken audio (mp3). Returns a status with byte size.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| voice | No | alloy |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions output format (mp3) and return value (status with byte size) but lacks details on execution behavior (e.g., async, latency), voice options, or any 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 concise with two short sentences, efficiently conveying the core functionality. However, it could include parameter details without becoming verbose.
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 and the existence of an output schema, the description covers basic return info but omits parameter details and usage guidance, leaving gaps for effective selection and use.
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 0% and the description does not explain the parameter 'text' or 'voice' (default 'alloy'). It provides no added meaning beyond the schema definition, which is insufficient for proper tool invocation.
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 converts text to spoken audio in mp3 format and returns status with byte size. It distinctly differentiates from sibling tools like transcribe_audio (which does the opposite) and other generation tools.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or scenarios where this tool is preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
transcribe_audioBInspect
Transcribe spoken audio (from a public URL) to text.
| Name | Required | Description | Default |
|---|---|---|---|
| audio_url | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It does not disclose potential behavioral traits like supported audio formats, maximum duration, rate limits, or whether the operation is synchronous (especially given sibling get_video_status suggests async patterns in the suite).
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?
Single sentence of 8 words, front-loaded with verb and resource. No wasted words; every word is essential.
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?
Although an output schema exists (not shown), the description lacks crucial context for a transcription tool: supported audio formats, maximum audio duration, public URL accessibility requirements, and whether the operation is asynchronous. Sibling get_video_status hints at async flows, but this is not clarified.
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?
Only one parameter (audio_url) with 0% schema description coverage. The description adds value by specifying it must be a 'public URL', but lacks details on expected formats, length constraints, or how to obtain a URL. Baseline 3 is appropriate for a single-parameter tool with some added context.
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 action (transcribe) and resource (spoken audio from public URL to text). It distinguishes from sibling tools like generate_image and text_to_speech, which perform different actions.
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 for public URLs, but it does not explicitly specify when to use this tool versus alternatives or provide exclusions. No guidance on prerequisites or scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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