Retrieval-Augmented Thinking MCP Server

Retrieval-Augmented Thinking MCP Server

Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.

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Knowledge & Memory
Research & Data
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A context-aware reasoning system that orchestrates structured thought processes through dynamic trajectories. Core Capabilities: - Maintains adaptive thought chains with branching and revision capabilities - Implements iterative hypothesis generation and validation cycles - Preserves context coherence across non-linear reasoning paths - Supports dynamic scope adjustment and trajectory refinement Reasoning Patterns: - Sequential analysis with backtracking capability - Parallel exploration through managed branch contexts - Recursive refinement via structured revision cycles - Hypothesis validation through multi-step verification Parameters: thought: Structured reasoning step that supports: • Primary analysis chains • Hypothesis formulation/validation • Branch exploration paths • Revision proposals • Context preservation markers • Verification checkpoints next_thought_needed: Signal for continuation of reasoning chain thought_number: Position in current reasoning trajectory total_thoughts: Dynamic scope indicator (adjustable) is_revision: Marks recursive refinement steps revises_thought: References target of refinement branch_from_thought: Indicates parallel exploration paths branch_id: Context identifier for parallel chains needs_more_thoughts: Signals scope expansion requirement Execution Protocol: 1. Initialize with scope estimation 2. Generate structured reasoning steps 3. Validate hypotheses through verification cycles 4. Maintain context coherence across branches 5. Implement revisions through recursive refinement 6. Signal completion on validation success The system maintains solution integrity through continuous validation cycles while supporting dynamic scope adjustment and non-linear exploration paths.

README

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:

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

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