mcp-server-llmling

LLMling Server Manual

Overview

mcp-server-llmling is a server for the Machine Chat Protocol (MCP) that provides a YAML-based configuration system for LLM applications.

LLMLing, the backend, provides a YAML-based configuration system for LLM applications. It allows to set up custom MCP servers serving content defined in YAML files.

Static Declaration: Define your LLM's environment in YAML - no code required
MCP Protocol: Built on the Machine Chat Protocol (MCP) for standardized LLM interaction
Component Types:
- Resources: Content providers (files, text, CLI output, etc.)
- Prompts: Message templates with arguments
- Tools: Python functions callable by the LLM

The YAML configuration creates a complete environment that provides the LLM with:

Access to content via resources
Structured prompts for consistent interaction
Tools for extending capabilities

Key Features

1. Resource Management

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

2. Tool System

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

3. Prompt Management

Static prompts with template support
Dynamic prompts from Python functions
File-based prompts
Prompt argument validation
Completion suggestions for prompt arguments

4. Multiple Transport Options

Stdio-based communication (default)
Server-Sent Events (SSE) / Streamable HTTP for web clients
Support for custom transport implementations

Usage

With 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": {}
    }
  }
}

With 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": {}
    }
  }
}

Manual Server Start

Start the server directly from command line:

# Latest version
uvx mcp-server-llmling@latest

1. 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())

2. 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": 3001,
                "cors_origins": ["http://localhost:3000"]
            }
        )
        await server.start()

asyncio.run(main())

3. Resource Configuration

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

  api_docs:
    type: text
    content: |
      API Documentation
      ================
      ...

4. Tool Configuration

tools:
  analyze_code:
    import_path: "mymodule.tools.analyze_code"
    description: "Analyze Python code structure"

toolsets:
  api:
    type: openapi
    spec: "https://api.example.com/openapi.json"

Tip

For OpenAPI schemas, you can install Redocly CLI to bundle and resolve OpenAPI specifications before using them with LLMLing. This helps ensure your schema references are properly resolved and the specification is correctly formatted. If redocly is installed, it will be used automatically.

Server Configuration

The server is configured through a YAML file with the following sections:

global_settings:
  timeout: 30
  max_retries: 3
  log_level: "INFO"
  requirements: []
  pip_index_url: null
  extra_paths: []

resources:
  # Resource definitions...

tools:
  # Tool definitions...

toolsets:
  # Toolset definitions...

prompts:
  # Prompt definitions...

MCP Protocol

The server implements the MCP protocol which supports:

Resource Operations
- List available resources
- Read resource content
- Watch for resource changes
Tool Operations
- List available tools
- Execute tools with parameters
- Get tool schemas
Prompt Operations
- List available prompts
- Get formatted prompts
- Get completions for prompt arguments
Notifications
- Resource changes
- Tool/prompt list updates
- Progress updates
- Log messages

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

A server for the Machine Chat Protocol (MCP) that provides a YAML-based configuration system for LLM applications, allowing users to define resources, tools, and prompts without writing code.

LLMling Server Manual

Related Resources

Reddit Discussion about this server

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