TUNDR
If you are the rightful owner of TUNDR and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.
The TUNDR MCP Optimization Server is a high-performance server designed for mathematical optimization tasks, focusing on Bayesian Optimization using Gaussian Processes.
The TUNDR MCP Optimization Server is a robust and scalable solution for handling complex mathematical optimization tasks. It implements the Model Context Protocol (MCP) to provide a standardized interface for defining, submitting, and monitoring optimization tasks. The server is particularly adept at Bayesian Optimization, leveraging Gaussian Processes to efficiently explore and exploit the search space. It supports multiple kernel types, parallel evaluations, and constrained optimization, making it suitable for a wide range of applications, from hyperparameter tuning in machine learning to engineering design optimization. The server is designed with reliability and scalability in mind, featuring comprehensive test coverage, structured logging, and support for distributed tracing and monitoring. It can be easily integrated into production environments, offering both RESTful and JSON-RPC 2.0 interfaces.
Features
- Bayesian Optimization: Supports multiple kernels, parallel evaluations, and constrained optimization for efficient global optimization of expensive black-box functions.
- Robust Implementation: Features comprehensive test coverage, graceful error handling, and detailed structured logging.
- Performance Optimizations: Includes fast matrix operations, efficient memory management, and optimized Cholesky decomposition.
- Monitoring & Observability: Provides Prometheus metrics, structured logging, distributed tracing, and health check endpoints.
- Scalability: Supports stateless design, horizontal scaling, and multiple storage backends.