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AtlasCloudAI

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

by AtlasCloudAI

List Atlas Cloud Models

atlas_list_models
Read-onlyIdempotent

Discover available AI models for text, image, and video generation on Atlas Cloud, with filtering by type to find specific models and their details.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoFilter by model type: Text, Image, or Video
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, open-world). The description adds useful context about the return format ('Markdown-formatted list of models grouped by type') and optional filtering, but does not disclose additional aspects like rate limits, authentication needs, or pagination behavior. No contradiction with annotations.

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 well-structured and front-loaded with the core purpose, followed by clear sections for Args, Returns, and Examples. Every sentence earns its place by providing essential information without redundancy, making it easy to scan and understand.

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?

Given the tool's low complexity (one optional parameter), rich annotations, and 100% schema coverage, the description is mostly complete. It explains the purpose, usage, and return format. However, without an output schema, it could benefit from more detail on the return structure (e.g., specific fields like pricing units).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (the single parameter 'type' is fully documented in the schema with enum values and description). The description adds minimal value beyond the schema by providing examples of parameter usage, but does not explain semantics or constraints not already in the schema. Baseline 3 is appropriate given high schema coverage.

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 specific action ('List all available models') and resource ('on Atlas Cloud'), and distinguishes it from siblings like atlas_get_model_info (which gets details for a specific model) and atlas_chat/atlas_generate_image (which use models rather than list them). The title reinforces this purpose.

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?

The description provides clear context for when to use optional filtering ('optionally filtered by type') and includes examples for different scenarios. However, it does not explicitly state when NOT to use this tool versus alternatives like atlas_get_model_info (for detailed info on a specific model) or atlas_search_docs (for documentation).

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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