Atlas Cloud MCP Server (Image / Video / LLM APIs)
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ATLASCLOUD_API_KEY | Yes | Atlas Cloud API Key used to authenticate with the AI API aggregation platform. Get one free at atlascloud.ai/console/api-keys |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| atlas_search_docsA | 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:
Returns: Markdown-formatted list of matching models with key information. Examples:
|
| atlas_list_modelsA | List all available models on Atlas Cloud, optionally filtered by type. Args:
Returns: Markdown-formatted list of models grouped by type, including model ID, description, provider, and pricing. Examples:
|
| atlas_get_model_infoA | 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:
Returns: Markdown-formatted model details including:
Examples:
|
| atlas_generate_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:
Returns: A prediction ID to check the result with atlas_get_prediction. Examples:
|
| atlas_generate_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:
Returns: A prediction ID to check the result with atlas_get_prediction. Video generation typically takes 1-5 minutes. Examples:
|
| atlas_chatA | Send a chat completion request to an LLM model via Atlas Cloud API (OpenAI-compatible format). Args:
Returns: The LLM response including the generated message, token usage, and finish reason. Examples:
|
| atlas_get_predictionA | 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:
Args:
Returns: The current status and output of the generation task. Examples:
|
| atlas_quick_generateA | 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:
Returns: A prediction ID to check the result with atlas_get_prediction. Examples:
|
| atlas_upload_mediaA | 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:
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:
Returns: The publicly accessible download URL of the uploaded file. Examples:
|
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
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.
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.
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.
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.