caddy-mcp 0.30.0

Caddy Reverse Proxy MCP Server for Agentic AI!

Caddy MCP

Caddy Reverse Proxy administrative and configuration orchestrator. Built with the highest architectural standards, incorporating dynamic facades, custom API routing, and FastMCP tool decoration.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Configuration
• MCP Tools
• Architecture
• Deployment
• Contributing
• License

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Overview

Caddy MCP provides a high-performance, model-optimized interface to Caddy capabilities. It isolates the model from underlying API transport complexity, ensuring safe, idempotent, and highly traceable system interactions.

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Features

• Dynamic Facade Orchestration: Integrates multi-inheritance clients cleanly under a single facade.
• Battle-Tested Resilience: Out-of-the-box credential authentication, connection polling, and request retry strategies.
• FastMCP Declarative Tools: Fast, native schema registration with full inline validation.
• Complete Test Intent Diversity: Deep, automated unit, integration, and mock tests ensuring high code coverage.

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⚙️ Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

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Installation

Install in editable mode directly inside your active workspace:

pip install -e .[all]

Or via the uv tool:

uv pip install -e .

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Usage

You can launch the FastMCP server in stdio mode via Python module execution:

import asyncio
from caddy_mcp.mcp_server import get_mcp_instance

async def main():
    mcp = get_mcp_instance()
    # Execute stdio loop or launch server
    print("MCP Server ready.")

if __name__ == "__main__":
    asyncio.run(main())

For direct shell launch, execute:

python -m caddy_mcp.mcp_server

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Configuration

The package is fully configurable via the environment variables listed below:

Variable	Description	Default	Required
CADDY_URL	Caddy Administration API URL endpoint	http://localhost:2019	Yes
CADDY_TOKEN	Optional bearer token if API is secured	your_secure_bearer_token	Yes

A local template is supplied inside .env.example. Copy this file as .env and fill out your specific service endpoint parameters before starting execution.

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MCP Tools

The following declarative FastMCP tools are registered and available to upstream AI agents:

Tool Name	Description	Parameters
get_config	Retrieve current Caddy configuration path	path: str
set_config	Update Caddy configuration path with new json value	path: str, data: dict
delete_config	Delete Caddy configuration path	path: str
load_config	Load Caddy configuration string	config_str: str, type: str

See docs/overview.md or docs/concepts.md for deeper operational examples.

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Architecture

This package uses the standardized Agent-Utilities dynamic facade architecture:

graph TD
    User([User Agent]) --> Server[FastMCP Server]
    Server --> Facade[Api Dynamic Facade]
    Facade --> ClientBase[ApiClientBase]
    Facade --> Auth[Credentials Auth Handler]
    ClientBase --> Service([External Service API])

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Deployment

Bare-Metal (Standard pip)

1. Set up your Python virtual environment (>= 3.10).
2. Install the package: pip install .[all]
3. Export credentials:

   export CADDY_URL="http://localhost:2019"
   

4. Run: python -m caddy_mcp.mcp_server

Container (Docker Compose)

A standard compose structure is provided inside the docker/ folder. Build and deploy:

docker compose -f docker/compose.yml up --build -d

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Contributing

Please audit all code changes against ecosystem guidelines in CONTRIBUTING.md if available, and run:

pre-commit run --all-files

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License

This project is licensed under the MIT License. See the LICENSE file for complete details.