phil65_mcp-server-llmling
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mcp-server-llmling is a server for the Machine Chat Protocol (MCP) that provides a YAML-based configuration system for LLM applications.
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)
- Text files (
- 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": ["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"
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:
-
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
