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Atlas Cloud MCP Server (Image / Video / LLM APIs)

Atlas Cloud MCP Server (Image / Video / LLM APIs)

by AtlasCloudAI

Supported Models

  • 🎬 Video (223) β€” Seedance 2.5 Β· MiniMax H3 Max Β· MiniMax H3 Fast Β· Gemini Omni 1.1 Flash Β· MiniMax H3 Β· Wan-3.0-Prime

  • 🎨 Image (145) β€” GPT Image 2.6 Sunburst Β· GPT Image 2.6 Flare Β· GPT Image 2.5 Sunburst Β· GPT Image 2.5 Flare

  • 🧊 3D (14) β€” Seed3D 2.0 Β· Tripo H3.1 Β· Hunyuan 3D Rapid Β· Hunyuan 3D Pro

  • πŸ’¬ LLM (83) β€” Hy4 Β· DeepSeek V4.1 Flash 1017 Β· Kimi K3.1 Pro HighSpeed Β· Kimi K3.1 Pro

  • πŸ”Š Audio (TTS Β· Music Β· ASR) (19) β€” Seed Audio 1.0 Β· MiniMax Speech 2.6 Turbo Β· MiniMax Speech 2.6 HD Β· xAI TTS v1

  • πŸ“š Explore more β€” all 517 live models Β»

🎬 Newest video models β€” Seedance 2.5 Β· Kling 4.0 Β· Wan 3.0 Β· Kling Video O3.

Related MCP server: universal-image-mcp

Contents

What You Can Do

Ask your AI assistant in plain language β€” it discovers the right model, builds the parameters, and submits the job:

  • 🎨 "Make a hero image for this blog post" β€” text-to-image across Nano Banana Pro, GPT Image 2, Flux 2, Seedream, Imagen…

  • 🎬 "Turn this product photo into a 5-second ad" β€” image-to-video with Seedance 2.5, Kling 3, Kling Video O3, Veo 3.1…

  • 🧊 "Make a 3D model from this photo" β€” image-to-3D / text-to-3D with Hunyuan 3D (GLB/OBJ/USDZ output)

  • πŸ”Š "Read this script aloud" β€” text-to-speech with Seed Audio, ElevenLabs, xAI TTS

  • 🎡 "Write a theme song for my app" β€” music generation with Suno, MiniMax Music

  • πŸ“ "Transcribe this meeting recording" β€” speech-to-text with Seed ASR, xAI STT

  • 🎞️ "Storyboard this script into 6 shots" β€” chain LLM β†’ image β†’ video inside one conversation

  • ✏️ "Edit this image β€” add a hat" β€” upload a local file, then run an image-editing model

  • πŸ’Έ "How much credit is left, and what did I spend this month?" β€” check balance, usage, and cost breakdowns

  • πŸ’¬ "What is in this screenshot?" β€” LLM chat with Claude, GPT, DeepSeek, Qwen, Gemini, GLM…, including image/video input on models that accept it

  • πŸ•“ "What did I generate yesterday?" β€” browse generation history and pull back the output URLs

Under the hood: model discovery, dynamic per-model parameter schemas (validated before every request so invalid params fail fast without spending credits, plus a dry_run flag on every generation tool that shows the exact request body without submitting it), media upload, one-step quick-generate, generation history, account balance & usage, and documentation search β€” all exposed as standard MCP tools (see Available Tools).

LLM calls are protocol-aware: each model is called through the contract it actually declares β€” OpenAI chat completions, OpenAI Responses, Anthropic Messages, or native Gemini generateContent β€” so Gemini-native models work without any special handling on your side.

Quick Start

Hosted server β€” no API key, nothing to install

Point your client at our hosted MCP server and sign in once in the browser. Nothing to install, no key to create or paste, and generation is billed to the Atlas Cloud account you sign in with.

Endpoint: https://mcp.atlascloud.ai/mcp

Claude Code

claude mcp add --transport http --scope user atlas-cloud https://mcp.atlascloud.ai/mcp

Drop --scope user to add it to the current project only. Adding the server does not sign you in β€” run /mcp inside Claude Code afterwards and follow the browser prompt.

Or add it to .mcp.json directly:

{
  "mcpServers": {
    "atlas-cloud": {
      "type": "http",
      "url": "https://mcp.atlascloud.ai/mcp"
    }
  }
}

type is required here β€” without it Claude Code reads the entry as a stdio server and skips it.

ChatGPT & Codex

Install the plugin, which bundles this server together with our skills:

codex plugin marketplace add AtlasCloudAI/atlas-cloud-plugin
codex plugin add atlas-cloud@atlas-cloud
codex mcp login atlas-cloud

In the ChatGPT desktop app: Settings β†’ Plugins β†’ Add β†’ Add plugin marketplace, source AtlasCloudAI/atlas-cloud-plugin, then open Atlas Cloud and install it.

VS Code

In .vscode/mcp.json:

{
  "servers": {
    "atlas-cloud": {
      "type": "http",
      "url": "https://mcp.atlascloud.ai/mcp"
    }
  }
}

Other MCP clients

Most take the endpoint under an mcpServers key. The exact field names differ between clients β€” some want the transport spelled out, others infer it from the URL β€” so check your client's own MCP documentation.

Authentication is OAuth 2.1: the client registers itself, opens your browser once, and stays authorized after that. There is no API key anywhere in this path.


Prefer to run the server on your own machine, with your own API key? The rest of this section covers that.

Prerequisites

See .env.example for the environment variable to set.

CLI agents (one-line install)

The fastest path β€” these AI coding agents add the server with a single command:

# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

# Goose CLI
goose mcp add atlascloud -- npx -y atlascloud-mcp

Set the ATLASCLOUD_API_KEY environment variable in your shell first.

IDEs, editors & extensions (JSON config)

Add this to your client's MCP configuration β€” works with every MCP-compatible client:

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": ["-y", "atlascloud-mcp"],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Client

Where to add it

Cursor

Settings β†’ MCP β†’ Add Server

Cline

MCP Marketplace β†’ Add Server

Continue

config.yaml β†’ MCP

Windsurf

Settings β†’ MCP β†’ Add Server

VS Code (Copilot)

.vscode/mcp.json or Settings β†’ MCP

Trae

Settings β†’ MCP β†’ Add Server

JetBrains IDEs

Settings β†’ Tools β†’ AI Assistant β†’ MCP

ChatGPT Desktop

Settings β†’ MCP

Amazon Q Developer

MCP Configuration

Roo Code

Settings β†’ MCP β†’ Add Server

Prefer Skills?

If you'd rather use Skills than MCP, we also ship an Atlas Cloud Skills package for Claude Code and other skill-compatible agents.

Available Tools

Tool

Description

atlas_search_docs

Search Atlas Cloud documentation and models by keyword

atlas_list_models

List available models, filtered by type (Text/Image/Video/Audio), sub-kind (3d/tts/stt/music/lyrics) or keyword

atlas_get_model_info

Get detailed model info including API schema, parameters, and usage examples

atlas_generate_image

Generate images and 3D models (image-to-3D / text-to-3D) with any supported Image model

atlas_generate_video

Generate videos with any supported video model

atlas_generate_audio

Generate audio β€” speech (TTS) and music/songs (Suno, MiniMax Music) β€” with any supported audio model

atlas_transcribe_audio

Transcribe speech to text (ASR) β€” meetings, interviews, voice notes

atlas_quick_generate

One-step image/video/audio generation β€” auto-finds model by keyword, builds params, and submits

atlas_upload_media

Upload a local image, audio, video or document and get a URL to pass to any model

atlas_chat

Chat with LLM models β€” endpoint and request format are picked automatically per model (OpenAI chat/responses, Anthropic messages, native Gemini); supports image/video/audio input

atlas_get_prediction

Check status and result of a generation task β€” media URLs, transcripts, lyrics, cover art and cost

atlas_list_predictions

Browse past generation tasks β€” recover a lost prediction ID or review earlier results

atlas_get_balance

Get the account balance and credit summary for your API key

atlas_get_model_usage

Get daily model usage (requests, tokens, image/video counts) over a date range

atlas_get_model_costs

Get daily model cost (spend) buckets over a date range

Usage Examples

Search for models

"Search Atlas Cloud for video generation models"

Your AI assistant will use atlas_search_docs or atlas_list_models to find relevant models.

Generate an image

"Generate an image of a cat in space using Seedream"

The assistant will:

  1. Use atlas_list_models to find Seedream image models

  2. Use atlas_get_model_info to get the model's parameters

  3. Use atlas_generate_image with the correct parameters

Generate a video

"Create a video of a rocket launch using Kling v3"

The assistant will:

  1. Find the Kling video model

  2. Get its schema to understand required parameters

  3. Use atlas_generate_video with appropriate parameters

Upload a local image for editing or video generation

"Edit this image /Users/me/photos/cat.jpg to add a hat"

The assistant will:

  1. Use atlas_upload_media to upload the local file and get a URL

  2. Find an image-editing model

  3. Use atlas_generate_image with the uploaded URL

Note: Uploaded files are for temporary use with Atlas Cloud generation tasks only. Files may be cleaned up periodically. Do not use this as permanent file hosting β€” abuse may result in API key suspension.

Generate speech (TTS)

"Read this sentence aloud with Seed Audio: Welcome to Atlas Cloud"

The assistant will:

  1. Use atlas_list_models with type="Audio" to find the TTS model

  2. Use atlas_generate_audio with the text to synthesize

  3. Use atlas_get_prediction to retrieve the generated audio URL

Generate music

"Make a 30-second upbeat synthwave track for my product demo with Suno"

Music models (Suno Chirp, MiniMax Music) are Audio-type models, so the assistant uses atlas_generate_audio with a song description (and optionally lyrics), then retrieves the audio URL via atlas_get_prediction.

Transcribe audio (speech-to-text)

"Transcribe this interview recording: https://example.com/interview.mp3"

The assistant uses atlas_transcribe_audio with a speech-to-text model (e.g., bytedance/seed-asr-2.0) and the audio_url, then retrieves the transcript via atlas_get_prediction. For local files, it first calls atlas_upload_media to get a URL.

Generate a 3D model

"Turn this product photo into a 3D model with Hunyuan 3D"

3D models are Image-type models, so the assistant uses atlas_generate_image with the image parameter and retrieves a GLB/OBJ/USDZ file via atlas_get_prediction.

Chat with an LLM

"Ask Qwen to explain quantum computing"

The assistant will use atlas_chat with the Qwen model. For a model that accepts images or video, attach them to the message and they are converted to that model's protocol automatically.

Check a call before paying for it

"Show me exactly what you'd send to Kling for this, don't run it yet"

Every generation tool takes dry_run: true β€” it resolves the model, builds the request, validates it against the model's schema, prints the exact JSON body, and stops. Nothing is submitted and nothing is billed. Useful when a media URL could land on more than one input field, or when a keyword could match several models.

Find an earlier generation

"What did I generate yesterday? Give me the video link again"

The assistant uses atlas_list_predictions to list recent tasks and atlas_get_prediction for the full result. This is also how to recover a task whose prediction ID was lost β€” the job keeps running (and is billed) even if the submitting call timed out.

Check balance and usage

"How much Atlas Cloud credit do I have left, and what did I spend this month?"

The assistant will use atlas_get_balance for the current balance and atlas_get_model_costs for the spend breakdown.

Development

# Install dependencies
npm install

# Build
npm run build

# Run in development mode
npm run dev

More Atlas Cloud Tools

License

MIT

Available Tools

9 tools
atlas_chatChat with LLMA

Send a chat completion request to an LLM model via Atlas Cloud API (OpenAI-compatible format).

Args:

  • model (string, required): The LLM model ID (e.g., "deepseek-ai/deepseek-v3.2", "qwen/qwen3-32b")

  • messages (array, required): Array of message objects with "role" and "content" fields. Roles: "system", "user", "assistant"

  • temperature (number, optional): Sampling temperature, 0-2. Default: 1

  • max_tokens (number, optional): Maximum tokens in the response

  • top_p (number, optional): Nucleus sampling parameter, 0-1. Default: 1

Returns: The LLM response including the generated message, token usage, and finish reason.

Examples:

  • model="deepseek-ai/deepseek-v3.2", messages=[{"role": "user", "content": "Hello"}]

  • model="qwen/qwen3-32b", messages=[{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Explain quantum computing"}], temperature=0.7

Inputs: model (required), messages (required), temperature, max_tokens, +1 more.

atlas_generate_imageGenerate ImageA

Generate an image using Atlas Cloud API.

This tool submits the generation request and returns immediately with a prediction ID. Use atlas_get_prediction to check the result later.

IMPORTANT: The "model" parameter requires an exact model ID (e.g., "seedream/seedream-v5.0-lite-text-to-image"). If you don't know the exact model ID, you MUST first call atlas_list_models with type="Image" to find it. Do NOT guess model IDs.

You should also use atlas_get_model_info to understand what parameters a specific image model accepts before calling this tool.

Args:

  • model (string, required): The exact image model ID. Use atlas_list_models to find valid IDs.

  • params (object, required): Model-specific parameters as a JSON object. Each model has different parameters defined in its schema. Common params include "prompt", "image_size", "num_inference_steps", etc. Use atlas_get_model_info to see the full parameter list for your chosen model.

Returns: A prediction ID to check the result with atlas_get_prediction.

Examples:

  • model="seedream/seedream-v5.0-lite-text-to-image", params={"prompt": "a cat in space"}

  • model="qwen-image/qwen-image-text-to-image-plus", params={"prompt": "sunset over mountains", "image_size": "1024x1024"}

Inputs: model (required), params (required).

atlas_generate_videoGenerate VideoA

Generate a video using Atlas Cloud API.

This tool submits the generation request and returns immediately with a prediction ID. Use atlas_get_prediction to check the result later.

IMPORTANT: The "model" parameter requires an exact model ID (e.g., "kling-video/kling-v3.0-standard-text-to-video"). If you don't know the exact model ID, you MUST first call atlas_list_models with type="Video" to find it. Do NOT guess model IDs.

You should also use atlas_get_model_info to see the full parameter list and schema for your chosen video model before calling this tool.

Args:

  • model (string, required): The exact video model ID. Use atlas_list_models to find valid IDs.

  • params (object, required): Model-specific parameters as a JSON object. Parameters vary by model - use atlas_get_model_info to see available params. Common ones include:

    • "prompt" (string): Text description of the video

    • "image_url" (string): Source image for image-to-video models

    • "duration" (number): Video duration in seconds

    • "aspect_ratio" (string): e.g., "16:9", "9:16"

Returns: A prediction ID to check the result with atlas_get_prediction. Video generation typically takes 1-5 minutes.

Examples:

  • model="kling-video/kling-v3.0-standard-text-to-video", params={"prompt": "a rocket launching into space", "duration": 5}

  • model="bytedance/seedance-v1.5-pro-image-to-video", params={"prompt": "camera panning right", "image_url": "https://example.com/photo.jpg"}

Inputs: model (required), params (required).

atlas_get_model_infoGet Model InfoA
Read-onlyIdempotent

Get detailed information about a specific Atlas Cloud model, including API documentation, input/output schema, pricing, and usage examples.

This tool fetches the model's OpenAPI schema and generates comprehensive API documentation with cURL examples.

Args:

  • model (string): The model ID (e.g., "deepseek-ai/deepseek-v3.2", "kling-video/kling-v3.0-standard-text-to-video")

Returns: Markdown-formatted model details including:

  • Model metadata (type, provider, context length, etc.)

  • Pricing information

  • Full API input/output schema with parameter descriptions

  • Required and optional parameters with defaults

  • cURL usage examples

  • Playground link

Examples:

  • model="deepseek-ai/deepseek-v3.2" -> DeepSeek V3.2 model details and API docs

  • model="kling-video/kling-v3.0-standard-text-to-video" -> Kling video model API docs

Inputs: model (required).

atlas_get_predictionGet Prediction ResultA
Read-onlyIdempotent

Check the status and result of an image/video generation task.

Use this after submitting a generation request to check if the result is ready.

If the status is still "processing" or "starting", wait a moment and try again.

When the result is ready (status is "completed" or "succeeded"), the output URLs will be returned. You should then:

  1. Show the output URLs to the user

  2. Ask the user if they want to download the file to their local machine (you can use curl or wget to download it)

Args:

  • prediction_id (string, required): The prediction ID returned from a generation request

Returns: The current status and output of the generation task.

Examples:

  • prediction_id="pred_abc123" -> check generation status

Inputs: prediction_id (required).

atlas_list_modelsList Atlas Cloud ModelsA
Read-onlyIdempotent

List all available models on Atlas Cloud, optionally filtered by type.

Args:

  • type (string, optional): Filter by model type. Options: "Text", "Image", "Video"

Returns: Markdown-formatted list of models grouped by type, including model ID, description, provider, and pricing.

Examples:

  • No params -> list all models

  • type="Image" -> list only image generation models

  • type="Video" -> list only video generation models

  • type="Text" -> list only LLM/text models

Inputs: type.

atlas_quick_generateQuick Generate Image/VideoA

One-step image or video generation - automatically finds the model by keyword, fetches its schema, builds parameters, and submits the task.

IMPORTANT: If this tool fails to find a model, call atlas_list_models first to get the exact model list, then use atlas_generate_image or atlas_generate_video with the exact model ID instead.

The tool searches for models by keyword matching against model ID, display name, and tags. After getting the prediction ID, use atlas_get_prediction to check the result.

Args:

  • model_keyword (string, required): A keyword to search for the model. Use the model's display name or key words (e.g., "Nano Banana", "Seedream", "Kling", "Vidu", "Seedance")

  • type (string, required): Generation type: "Image" or "Video"

  • prompt (string, required): Text description of what to generate

  • image_url (string, optional): Source image URL for image-to-video or image editing models

  • extra_params (object, optional): Additional model-specific parameters to override defaults (e.g., {"duration": 10, "aspect_ratio": "16:9"})

Returns: A prediction ID to check the result with atlas_get_prediction.

Examples:

  • model_keyword="nano banana", type="Image", prompt="a cute cat in space"

  • model_keyword="seedream v5", type="Image", prompt="sunset over mountains"

  • model_keyword="kling v3", type="Video", prompt="a rocket launching", extra_params={"duration": 5}

  • model_keyword="seedance", type="Video", prompt="camera panning right", image_url="https://example.com/photo.jpg"

Inputs: model_keyword (required), type (required), prompt (required), image_url, +1 more.

atlas_search_docsSearch Atlas Cloud DocsA
Read-onlyIdempotent

Search Atlas Cloud documentation, models, and API references by keyword.

Returns matching models with descriptions, pricing, and links. For detailed API docs of a specific model, use atlas_get_model_info instead.

Args:

  • query (string): Search keyword to match against model names, types, providers, tags, etc.

Returns: Markdown-formatted list of matching models with key information.

Examples:

  • "video generation" -> finds all video generation models

  • "deepseek" -> finds all DeepSeek models

  • "image edit" -> finds image editing models

  • "qwen" -> finds all Qwen models

Inputs: query (required).

atlas_upload_mediaUpload Media FileA

Upload a local image or media file to Atlas Cloud and get a publicly accessible URL.

Use this tool when you need to provide an image URL to image-editing or image-to-video models, but only have a local file path.

Workflow:

  1. Upload the local file with this tool to get a URL

  2. Use the returned URL as the "image_url" parameter in atlas_generate_image, atlas_generate_video, or atlas_quick_generate

Supported file types: images (jpg, png, webp, etc.), videos, and other media files.

IMPORTANT: This upload is intended for temporary use with Atlas Cloud generation tasks only. Uploaded files may be cleaned up periodically. Do NOT use this as a permanent file hosting service. Abuse (e.g., bulk uploads unrelated to generation tasks) may result in API key suspension.

Args:

  • file_path (string, required): Absolute path to the local file to upload

Returns: The publicly accessible download URL of the uploaded file.

Examples:

Inputs: file_path (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.4/5.0

    Scored across 9 tools

    Disambiguation4/5

    Most tools have distinct purposes, but there is some overlap between atlas_quick_generate and the specific generation tools (atlas_generate_image, atlas_generate_video). The quick_generate tool serves as a convenience wrapper that could cause confusion about when to use it versus the more explicit tools, though descriptions clarify its role as a fallback. Other tools like atlas_list_models, atlas_get_model_info, and atlas_search_docs have clear, non-overlapping functions for discovery and documentation.

    Naming Consistency5/5

    All tool names follow a consistent 'atlas_' prefix with snake_case naming, using descriptive verb_noun patterns (e.g., atlas_chat, atlas_generate_image, atlas_list_models). This uniformity makes the tool set predictable and easy to navigate, with no deviations in style or convention across the nine tools.

    Tool Count5/5

    With 9 tools, the count is well-scoped for the server's purpose of accessing image, video, and LLM APIs. It covers core operations like generation (chat, image, video), model discovery (list, info, search), task management (get_prediction), and utilities (upload_media), without being excessive or lacking essential functionality.

    Completeness5/5

    The tool set provides complete coverage for the domain, including CRUD-like operations for generation tasks (create via generate tools, read via get_prediction), comprehensive model discovery and documentation (list_models, get_model_info, search_docs), and necessary utilities (upload_media). There are no obvious gaps; agents can handle full workflows from model selection to result retrieval.

    Maintenance

    ActivityActive
    ResponsivenessOutdated data

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