Mandoline
Empowers large language models and AI assistants to conduct self-evaluations, critique their performance, and continuously improve through a standardized evaluation framework.
About
The Mandoline server provides a robust evaluation framework, enabling AI assistants like Claude Code, Claude Desktop, and Cursor to deeply reflect on, critique, and enhance their own performance. By leveraging the Model Context Protocol (MCP), it facilitates LLMs in self-evaluation, allowing users to define custom evaluation criteria (metrics) and efficiently score prompt/response pairs. This system supports continuous improvement for AI agents by tracking evaluation history and providing tools for managing evaluation data.
Key Features
- 2 GitHub stars
- Define and manage custom evaluation metrics
- Seamless integration with popular AI assistants like Claude Code, Claude Desktop, and Cursor
- Score prompt and response pairs against defined criteria
- Enables LLM self-evaluation using the Model Context Protocol (MCP)
- Browse and filter historical evaluation results
Use Cases
- Creating and managing custom evaluation criteria for specific AI tasks and use cases
- Enabling LLMs to reflect on, critique, and continuously improve their operational performance
- Integrating AI evaluation capabilities directly into large language models and AI assistants
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