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

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

by AtlasCloudAI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Search Atlas Cloud Docs

atlas_search_docs
Read-onlyIdempotent

Search Atlas Cloud documentation, models, and API references by keyword to find AI models for image generation, video generation, and LLMs with pricing and links.

Instructions

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

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "atlas_search_docs"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesSearch keyword to match against model names, types, providers, tags

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, covering safety and behavior. The description adds useful context by specifying the return format (Markdown-formatted list) and content (models with descriptions, pricing, links), which goes beyond annotations. 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 usage guidelines, parameter info, return format, and examples. Every sentence adds value without waste, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, 100% schema coverage), rich annotations, and no output schema, the description is complete. It covers purpose, usage, return format, and examples, providing sufficient context for an agent to use the tool effectively without over-explaining.

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%, with the schema fully documenting the single 'query' parameter. The description adds minimal extra meaning by listing examples of what the query can match (e.g., model names, types, providers, tags), but this is largely redundant with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 searches Atlas Cloud documentation, models, and API references by keyword, specifying it returns matching models with descriptions, pricing, and links. It distinguishes from sibling atlas_get_model_info by noting that tool is for detailed API docs of a specific model, making the purpose specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides when to use this tool (search by keyword across models, types, providers, tags) and when not to use it (for detailed API docs of a specific model, use atlas_get_model_info instead), offering clear alternatives and context.

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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