Sequential Thinking LP Solver
Solves Linear Programming and Non-Linear Programming problems by breaking them into systematic, verifiable steps using the Sequential Thinking Model Context Protocol.
About
Sequential Thinking LP Solver provides a structured approach to solving complex Linear Programming (LP) and Non-Linear Programming (NLP) problems. It leverages the Model Context Protocol (MCP), an open standard that facilitates secure, two-way connections between AI models like Anthropic's Claude and various tools or data sources. This client-server architecture enables users to systematically break down optimization challenges, maintain solution context throughout the process, and revise their approach as needed, enhancing the flexibility and verifiability of problem-solving.
Key Features
- Structured problem-solving for Linear and Non-Linear Programming
- Leverages Model Context Protocol (MCP) for AI integration
- Breaks down complex problems into systematic, verifiable steps
- Supports revision and adaptation of problem-solving paths
- Maintains solution context throughout the problem-solving process
- 4 GitHub stars
Use Cases
- Optimizing resource allocation in manufacturing (Linear Programming)
- Strategizing marketing budget distribution (Non-Linear Programming)
- Applying structured thinking to complex optimization challenges
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