LLMLing

Provides a YAML-driven MCP server for configuring LLM resources, prompts, and tools

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "mcp-mirror-phil65_mcp-server-llmling": {
      "command": "uvx",
      "args": [
        "mcp-server-llmling",
        "start",
        "path/to/your/config.yml"
      ]
    }
  }
}

You can run a self-contained MCP server that exposes resources, prompts, and tools for your LLM applications using YAML-based configuration. This server follows the Machine Chat Protocol (MCP) to provide standardized interactions between your LLM and configured content, prompts, and callable tools.

How to use

To interact with the server from your MCP client, start the server with a local runtime and point it at your YAML configuration. You will get access to defined resources (content like text files or code), prompts (templates with arguments), and tools (Python callables or OpenAPI-based endpoints) that your LLM can invoke during conversations.

How to install

Prerequisites and initial setup are focused on running the MCP server locally with a standard CLI workflow.

# Start the server with a local configuration file using the runtime CLI
uvx mcp-server-llmling start path/to/your/config.yml

# Or start a latest published version without a config (for quick testing)
uvx mcp-server-llmling@latest

Additional sections

Server configuration is defined in YAML and includes sections for global settings, resources, tools, toolsets, and prompts. The MCP protocol enables you to list, read, and watch resources; list and execute tools with parameters; and format prompts or obtain completions for prompt arguments. Resources can include text, files, CLI output, code, or images, and can be watched for hot-reload. Tools can be Python callables or OpenAPI-defined endpoints, with parameter validation and structured responses.

Example server usage patterns you can implement in your client include: - Loading resources to provide context to the LLM. - Using prompts to standardize interactions and ensure consistent responses. - Invoking tools to perform computations or fetch data during a chat session. - Watching resources to refresh the LLM’s context when files change.

Available tools

analyze_code

Analyze Python code structure