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Shannon Thinking MCP Server

by olaservo
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TypeScript
MIT License
22
36
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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:

  1. Problem Definition: Strip the problem to its fundamental elements
  2. Constraints: Identify system limitations and boundaries
  3. Model: Develop mathematical/theoretical frameworks
  4. Proof/Validation: Validate through formal proofs or experimental testing
  5. 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

Tool Schema

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
Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

Tools

一种实施克劳德·香农问题解决方法的工具，帮助将复杂问题分解为结构化步骤，包括问题定义、约束、建模、验证和实施。

  1. 概述
    1. 安装
      1. NPX
    2. 用法
      1. 示例用法
    3. 特征
      1. 发展
        1. 工具架构
          1. 何时使用

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