📌Overview

Purpose: The MCP server aims to enhance AI model capabilities by integrating structured, retrieval-augmented thinking processes for improved reasoning and problem-solving.

Overview: This server provides a framework for dynamic thought chains, facilitating parallel exploration paths and recursive refinement cycles. It supports advanced reasoning with features that maintain context and coherence, enabling users to efficiently navigate complex problem-solving scenarios.

Key Features:

• Adaptive Thought Chains: Maintains coherent reasoning flows with the ability to branch and revise thoughts, enhancing adaptability in thought processes.

• Iterative Hypothesis Generation: Incorporates validation cycles for rigorous hypothesis testing, ensuring continuous improvement in reasoning accuracy.

• Dynamic Scope Adjustment: Allows for flexible exploration and contextual refinement, making it easier to adjust the focus of thought chains based on evolving insights.

• Quality Assessment: Provides real-time evaluation of thought processes, aiding in the enhancement of overall reasoning quality.

• Branch Management: Efficiently handles multiple parallel exploration paths, ensuring that various reasoning avenues can be pursued simultaneously.

• Revision Tracking: Manages recursive refinement cycles to improve clarity and depth in thought processes.

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Retrieval-Augmented Thinking MCP Server

An MCP (Model Context Protocol) server implementation that enhances AI model capabilities with structured, retrieval-augmented thinking processes. This server enables dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning and problem-solving.

Features

• Adaptive Thought Chains: Maintains coherent reasoning flows with branching and revision capabilities
• Iterative Hypothesis Generation: Implements validation cycles for hypothesis testing
• Context Coherence: Preserves context across non-linear reasoning paths
• Dynamic Scope Adjustment: Supports flexible exploration and refinement
• Quality Assessment: Real-time evaluation of thought processes
• Branch Management: Handles parallel exploration paths
• Revision Tracking: Manages recursive refinement cycles

Installation

npm install @modelcontextprotocol/server-retrieval-augmented-thinking

Usage

Command Line

mcp-server-retrieval-augmented-thinking

Programmatic Usage

import { Server } from '@modelcontextprotocol/sdk/server';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio';

// Initialize and run the server
const server = new Server({
  name: 'retrieval-augmented-thinking',
  version: '0.1.0'
});

// Connect transport
const transport = new StdioServerTransport();
await server.connect(transport);

Tool Configuration

The server provides a tool with the following parameters:

• thought (string): Current reasoning step
• thoughtNumber (number): Position in reasoning chain
• totalThoughts (number): Estimated scope
• nextThoughtNeeded (boolean): Chain continuation signal
• isRevision (boolean, optional): Marks refinement steps
• revisesThought (number, optional): References target thought
• branchFromThought (number, optional): Branch origin point
• branchId (string, optional): Branch identifier
• needsMoreThoughts (boolean, optional): Scope expansion signal

Advanced Features

Thought Chain Analytics

The server tracks various metrics for thought chain quality, including:

• Chain effectiveness
• Revision impact
• Branch success rate
• Overall quality
• Individual thought metrics such as complexity, depth, quality, and impact

Pattern Recognition

Analyzes thought patterns for:

• Reasoning structures
• Context preservation
• Hypothesis validation
• Solution coherence

Development

# Build
npm run build

# Watch mode
npm run watch

Contributing

Contributions welcome! Please read our contributing guidelines and submit pull requests.

License

MIT