Skip to main content
Make these docs available to your AI tools for real-time answers about AI, machine learning, computer vision, and robotics.

MCP Server

Connect AI tools directly to our documentation

llms.txt

Documentation index for AI discovery

​
Model Context Protocol (MCP)

The Model Context Protocol is an open standard that connects AI applications to external data sources. Our MCP server lets AI tools search and retrieve documentation in real-time. MCP Server URL: https://aegean.ai/mcp

​
Claude Code

claude mcp add --transport http docs-aegean https://aegean.ai/mcp
To make it available globally: 
claude mcp add --transport http --scope user docs-aegean https://aegean.ai/mcp

​
Claude Desktop

  1. Open Claude Desktop settings
  2. Navigate to Connectors
  3. Select Add custom connector
  4. Enter:
    • Name: aegean.ai docs
    • URL: https://aegean.ai/mcp
  5. Select Add

​
VS Code

Create or edit .vscode/mcp.json in your workspace: 
{
  "servers": {
    "aegean-docs": {
      "type": "http",
      "url": "https://aegean.ai/mcp"
    }
  }
}

​
Cursor

  1. Open Command Palette (Cmd+Shift+P / Ctrl+Shift+P)
  2. Search for “Open MCP settings”
  3. Select Add custom MCP
  4. Add to mcp.json:
{
  "aegean-docs": {
    "type": "http",
    "url": "https://aegean.ai/mcp"
  }
}

​
LLMs.txt

The llms.txt standard helps AI tools index documentation efficiently.
Course lecture content is available through the MCP server but not in the public llms.txt index. Connect via MCP for full access to course materials.

​
Contextual Menu

Every page on this site includes a contextual menu (top-right corner) with options to:
  • Copy page - Copy as Markdown for AI context
  • View as Markdown - See the raw content
  • Open in ChatGPT/Claude/Perplexity - Start a conversation with page context
  • Connect to Cursor/VS Code - Install MCP server directly

​
What You Can Ask

Once connected, ask questions like:
  • “How do transformers work?”
  • “Explain the attention mechanism”
  • “What is policy gradient in reinforcement learning?”
  • “How do CNNs detect objects?”
  • “What’s the difference between SLAM and visual odometry?”
Edit this page on GitHub or file an issue.