docker build -t url-agent .
docker run --rm -i -e OPENAI_API_KEY=your_key url-agent

# Install dependencies
uv sync

# Run the server
uv run urlagent

MODEL_PROVIDER=openai              # openai, anthropic, or ollama
OPENAI_MODEL=gpt-4o-mini          # Model name for any provider
OPENAI_API_KEY=your_key           # For OpenAI
ANTHROPIC_API_KEY=your_key        # For Anthropic
OLLAMA_BASE_URL=http://localhost:11434/v1  # For Ollama (optional)

ollama pull llama3
export MODEL_PROVIDER=ollama
export OPENAI_MODEL=llama3
uv run urlagent

export MODEL_PROVIDER=anthropic
export OPENAI_MODEL=claude-3-5-sonnet-20241022
export ANTHROPIC_API_KEY=your_key
uv run urlagent

{
  "mcpServers": {
    "url-agent": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "-e", "OPENAI_API_KEY=YOUR_KEY", "url-agent"]
    }
  }
}

{
  "mcpServers": {
    "url-agent": {
      "command": "urlagent",
      "env": {
        "OPENAI_API_KEY": "YOUR_KEY"
      }
    }
  }
}

Use summarize_url on https://example.com

summarize_url(
  url: str,           # URL to analyze
  max_depth: int = 2, # Maximum crawl depth
  max_pages: int = 4  # Maximum pages to fetch
)

url-agent/
  src/url_agent/      # Main package
    server.py         # MCP server entry point
    react_agent.py    # ReAct loop logic
    web_fetcher.py    # Web scraping utilities
    providers/        # LLM provider implementations
  pyproject.toml
  Dockerfile