Retrieval-Augmented Thinking
Integrates with
Retrieval-Augmented Thinking
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 stepthoughtNumber
(number): Position in reasoning chaintotalThoughts
(number): Estimated scopenextThoughtNeeded
(boolean): Chain continuation signalisRevision
(boolean, optional): Marks refinement stepsrevisesThought
(number, optional): References target thoughtbranchFromThought
(number, optional): Branch origin pointbranchId
(string, optional): Branch identifierneedsMoreThoughts
(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