Phil65_mcp Server Llmling

PyPI License Package status Daily downloads Weekly downloads Monthly downloads Distribution format Wheel availability Python version Implementation Releases Github Contributors Github Discussions Github Forks Github Issues Github Issues Github Watchers Github Stars Github Repository size Github last commit Github release date Github language count Github commits this week Github commits this month Github commits this year Package status Code style: black PyUp Read the documentation!

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) 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": 8000,
 "cors_origins": ["
 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: "
 namespace: "api"

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:

1. Resource Operations

• List available resources
• Read resource content
• Watch for resource changes

2. Tool Operations

• List available tools
• Execute tools with parameters
• Get tool schemas

3. Prompt Operations

• List available prompts
• Get formatted prompts
• Get completions for prompt arguments

4. Notifications

• Resource changes
• Tool/prompt list updates
• Progress updates
• Log messages