Phil65 MCP Server Llmling

🚀 LLMLing Server Usage Guide

LLMLing Server is a powerful tool that offers a wide range of features for resource management, tool system integration, prompt management, and more. This guide will walk you through its various functionalities and how to use them effectively.

✨ Features

Resource Management

• Load and manage different types of resources:
  • Text files (PathResource)
  • Plain text content (TextResource)
  • CLI command outputs (CLIResource)
  • Python source code (SourceResource)
  • Python callable results (CallableResource)
  • Images (ImageResource)
• Support for resource hot reloading/auto-reloading
• Resource processing pipelines
• URI-based resource access

Tool System

• Register and execute Python functions as LLM tools
• Support for OpenAPI-based tools
• Entry point discovery tools
• Tool validation and parameter checking
• Structured tool responses

Prompt Management

• Static prompts with template support
• Dynamic prompts from Python functions
• File-based prompts
• Prompt parameter validation
• Prompt parameter auto-completion

Multiple Transport Options

• Standard input/output communication (default)
• Server-Sent Events (SSE) for web clients
• Support for custom transport implementations

MCP Protocol Support

• Support the communication protocol between devices to enable data interaction and control between devices.
• Provide multiple data transmission methods, such as device status query and command issuance.

📦 Installation

No specific installation steps provided in the original document.

💻 Usage Examples

Using in Zed Editor

Add LLMLing as a context server in your settings.json:

{
  "context_servers": {
    "llmling": {
      "command": {
        "env": {},
        "label": "llmling",
        "path": "uvx",
        "args": [
          "mcp-server-llmling",
          "start",
          "path/to/your/config.yml"
        ]
      },
      "settings": {}
    }
  }
}

Using in Claude Desktop

Configure LLMLing in your claude_desktop_config.json:

{
  "mcpServers": {
    "llmling": {
      "command": "uvx",
      "args": [
        "mcp-server-llmling",
        "start",
        "path/to/your/config.yml"
      ],
      "env": {}
    }
  }
}

Manually Starting the Server

Start the server directly from the command line:

# Use the latest version
uvx mcp-server-llmling@latest

Programmatic Usage

from llmling import RuntimeConfig
from mcp_server_llmling import LLMLingServer

async def main() -> None:
    async with RuntimeConfig.open(config) as runtime:
        server = LLMLingServer(runtime, enable_injection=True)
        await server.start()

asyncio.run(main())

Using Custom Transport

from llmling import RuntimeConfig
from mcp_server_llmling import LLMLingServer

async def main() -> None:
    async with RuntimeConfig.open(config) as runtime:
        server = LLMLingServer(
            config,
            transport="sse",
            transport_options={
                "host": "localhost",
                "port": 8000,
                "cors_origins": ["http://localhost:3000"]
            }
        )
        await server.start()

asyncio.run(main())

Resource Configuration

resources:
  python_code:
    type: path
    path: "./src/**/*.py"
    watch:
      enabled: true
      patterns:
        - "*.py"
        - "!**/__pycache__/**"

  api_docs:
    type: text
    content: |
      API Documentation
      ------

📚 Documentation

Configuration Explanation

# Server configuration example
server:
  host: "0.0.0.0"  # The host address to which the service is bound
  port: 8000        # The port on which the service listens

# Resource management configuration
resources:
  max_connections: 100  # Maximum number of allowed connections
  timeout: 30s          # Connection timeout

By following the above configurations and usage methods, you can easily integrate and manage the LLMLing server to meet the requirements of different scenarios.