just-prompt is a Model Control Protocol (MCP) server designed to offer a unified interface for interacting with multiple Large Language Model (LLM) providers. It simplifies prompt management by abstracting the complexities of different API calls. By leveraging the MCP protocol, just-prompt allows users to easily switch between various LLMs such as OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama, without needing to modify their code significantly. This simplifies experimentation and deployment.
The project's key feature is its unified API, eliminating the need to learn and interact with individual LLM provider APIs. It supports both text and file-based prompts, and enables running multiple models concurrently. Automatic model name correction and straightforward integration with Claude Code further enhance usability. Its modular design facilitates easy addition of new providers and models.
- Unified API: Simplifies interaction with multiple LLM providers.
- Flexible Prompting: Supports text and file-based prompts.
- Parallel Execution: Enables running multiple models concurrently.
- Model Correction: Automatically corrects model names for easier use.
- File Handling: Allows saving responses to files.
- Provider Listing: Provides simple tools for discovering available models.
- Extensible Architecture: Designed for easy addition of new providers.
just-prompt is an actively developed project with a clear structure and documentation. Recent commits and issue activity suggest ongoing maintenance and improvements. The presence of example configurations and clear installation instructions indicates a user-friendly development experience. The project is well documented and provides clear guidance on setup and use within the claude code environment.
This project benefits developers and researchers who need to work with multiple LLMs. It addresses use cases such as comparing model outputs, deploying to different providers, and streamlining AI workflows. By providing a consistent interface, just-prompt reduces complexity and facilitates experimentation with various LLM technologies, offering a practical alternative to managing separate API integrations.