Integrates with Codecov for tracking code coverage statistics and reporting.
Supports GitHub integration for repository management, tracking issues, pulls, stars and other GitHub project metrics.
Integrates with PyPI for package distribution and management information.
Connects with PyUp for monitoring and managing Python dependencies.
Uses YAML for configuration of LLM environments, including resources, prompts, tools, and global settings.
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) / Streamable HTTP for web clients
- Support for custom transport implementations
Usage
With Zed Editor
Add LLMLing as a context server in your settings.json
:
With Claude Desktop
Configure LLMLing in your claude_desktop_config.json
:
Manual Server Start
Start the server directly from command line:
1. Programmatic usage
2. Using Custom Transport
3. Resource Configuration
4. Tool Configuration
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:
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
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.
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