Shannon Thinking

A tool for systematic problem-solving based on Claude Shannon's methodology, breaking down complex problems into structured thoughts.

An MCP server demonstrating Claude Shannon's systematic problem-solving methodology. This server provides a tool that helps break down complex problems into structured thoughts following Shannon's approach of problem definition, mathematical modeling, and practical implementation.

Claude Shannon, known as the father of information theory, approached complex problems through a systematic methodology:
- Problem Definition: Strip the problem to its fundamental elements
- Constraints: Identify system limitations and boundaries
- Model: Develop mathematical/theoretical frameworks
- Proof/Validation: Validate through formal proofs or experimental testing
- Implementation/Experiment: Design and test practical solutions

This MCP server demonstrates this methodology as a tool that helps guide systematic problem-solving through these stages.

{ "mcpServers": { "shannon-thinking": { "command": "npx", "args": [ "-y", "server-shannon-thinking@latest" ] } } }

The server provides a single tool namedshannonthinkingthat structures problem-solving thoughts according to Shannon's methodology.

- The actual thought content
- Type (problem_definition/constraints/model/proof/implementation)
- Thought number and total thoughts estimate
- Confidence level (uncertainty: 0-1)
- Dependencies on previous thoughts
- Explicit assumptions
- Whether another thought step is needed

- Revision: Thoughts can revise earlier steps as understanding evolves
- Recheck: Mark steps that need re-examination with new information
- Experimental Validation: Support for empirical testing alongside formal proofs
- Implementation Notes: Practical constraints and proposed solutions

const thought = { thought: "The core problem can be defined as an information flow optimization", thoughtType: "problem_definition", thoughtNumber: 1, totalThoughts: 5, uncertainty: 0.2, dependencies: [], assumptions: ["System has finite capacity", "Information flow is continuous"], nextThoughtNeeded: true, // Optional: Mark as revision of earlier definition isRevision: false, // Optional: Indicate step needs recheck recheckStep: { stepToRecheck: "constraints", reason: "New capacity limitations discovered", newInformation: "System shows non-linear scaling" } }; // Use with MCP client const result = await client.callTool("shannonthinking", thought);

- Iterative Problem-Solving: Supports revisions and rechecks as understanding evolves
- Flexible Validation: Combines formal proofs with experimental validation
- Dependency Tracking: Explicitly tracks how thoughts build upon previous ones
- Assumption Management: Requires clear documentation of assumptions
- Confidence Levels: Quantifies uncertainty in each step
- Rich Feedback: Formatted console output with color-coding, symbols, and validation results

# Install dependencies npm install # Build npm run build # Run tests npm test # Watch mode during development npm run watch

The tool accepts thoughts with the following structure:

interface ShannonThought { thought: string; thoughtType: "problem_definition" | "constraints" | "model" | "proof" | "implementation"; thoughtNumber: number; totalThoughts: number; uncertainty: number; // 0-1 dependencies: number[]; assumptions: string[]; nextThoughtNeeded: boolean; // Optional revision fields isRevision?: boolean; revisesThought?: number; // Optional recheck field recheckStep?: { stepToRecheck: ThoughtType; reason: string; newInformation?: string; }; // Optional validation fields proofElements?: { hypothesis: string; validation: string; }; experimentalElements?: { testDescription: string; results: string; confidence: number; // 0-1 limitations: string[]; }; // Optional implementation fields implementationNotes?: { practicalConstraints: string[]; proposedSolution: string; }; }

This thinking pattern is particularly valuable for:

- Complex system analysis
- Information processing problems
- Engineering design challenges
- Problems requiring theoretical frameworks
- Optimization problems
- Systems requiring practical implementation
- Problems that need iterative refinement
- Cases where experimental validation complements theory

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