LLMling
About
An MCP server with an LLMling backend that uses YAML files to configure LLM applications.
Details
- License
- MIT
Explore
- Static YAML declaration – no code required
- Resources: files, text, CLI output, images, Python callables
- Tools: register Python functions or OpenAPI specs
- Prompts: static templates or dynamic Python functions
- Multiple transports: stdio, SSE, Streamable HTTP
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
LLMlingCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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())
resources:
python_code:
type: path
path: "./src//.py"
watch:
enabled: true
patterns:
- ".py"
- "!/__pycache__/**"
api_docs:
type: text
content: |
API Documentation
================
...
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.
The server is configured through a YAML file with the following sections:
```yaml
global_settings:
timeout: 30
max_retries: 3
log_level: "INFO"
requirements: []
pip_index_url: null
extra_paths: []
resources:
- 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
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"llmling": {
"mcp-server-llmling": {
"command": "uvx",
"args": [
"mcp-server-llmling@latest"
]
}
}
}
}McpServers
{
"mcp-server-llmling": {
"command": "uvx",
"args": [
"mcp-server-llmling@latest"
]
}
}
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
