Retrieval Augmented Thinking MCP Servers

Overview

Retrieval Augmented Thinking MCP servers provide interfaces for LLMs to enhance their responses with relevant information retrieved from external knowledge bases. These servers enable AI models to combine their general knowledge with specific, accurate information from trusted sources.

Core Components

Knowledge Retrieval Server

class RAGServer extends MCPServer {
  capabilities = {
    tools: {
      'queryKnowledge': async (params) => {
        // Query vector database
      },
      'updateContext': async (params) => {
        // Update context with retrieved info
      },
      'rankRelevance': async (params) => {
        // Rank retrieved passages
      }
    },
    resources: {
      'knowledgeBase': async () => {
        // Access knowledge sources
      }
    }
  }
}

Implementation Examples

Context Management

class ContextManager extends MCPServer {
  async initialize() {
    return {
      tools: {
        'embedDocument': this.handleEmbedding,
        'searchSimilar': this.findSimilarContent,
        'mergeContext': this.combineInformation
      }
    };
  }

  private async handleEmbedding({ content, metadata }) {
    // Implement document embedding
  }
}

Configuration Options

retrieval:
  vectorStore: "pinecone"  # or milvus, qdrant
  embeddingModel: "openai"
  contextWindow: 4096
  
augmentation:
  maxResults: 5
  minRelevance: 0.75
  sourcePriority: ["docs", "kb", "web"]

Security Guidelines

1. Data Access

   • Source verification
   • Access control
   • Usage tracking

2. Content Filtering

   • Relevance checking
   • Source validation
   • Information freshness

Common Use Cases

1. Knowledge Enhancement

   • Fact verification
   • Source citation
   • Context expansion

2. Information Retrieval

   • Document search
   • Semantic matching
   • Multi-source fusion

3. Content Generation

   • Research assistance
   • Documentation generation
   • Answer synthesis

Best Practices

1. Knowledge Management

   • Index maintenance
   • Content updates
   • Version control

2. Query Optimization

   • Search strategies
   • Result ranking
   • Context windowing

Testing Strategies

1. Retrieval Testing

   • Search accuracy
   • Response relevance
   • Context quality

2. Integration Testing

   • Vector store connectivity
   • Embedding generation
   • Result merging