shannon-thinking
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.
Overview
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.
Installation
NPX
{
"mcpServers": {
"shannon-thinking": {
"command": "npx",
"args": [
"-y",
"server-shannon-thinking@latest"
]
}
}
}
Usage
The server provides a single tool named shannonthinking that structures problem-solving thoughts according to Shannon's methodology.
Each thought must include:
- 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
Additional capabilities:
- 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
Example Usage
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);
Features
- 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
Development
# 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;
};
}
When to Use
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