Conversation Search MCP Server

Version: 1.1.0
Status: Production Ready
Last Updated: 2025-01-07

Overview

Advanced MCP server providing semantic and traditional search capabilities across Claude Code conversation history. Features vector embeddings, hybrid search, and comprehensive conversation management tools.

🚀 Key Features

Search Capabilities

• Traditional Search: Fast FTS-based keyword search with session indexing

• Vector Search: Semantic similarity using OpenAI embeddings

• Hybrid Search: Combined semantic + keyword matching for optimal results

• Context Retrieval: Adjacent chunk expansion for full conversation context

Conversation Management

• Recent Conversations: Optimized retrieval with project filtering

• Session Details: Full conversation history with message threading

• Auto-Naming: AI-powered conversation title generation

• Batch Operations: Bulk renaming and processing capabilities

Database Operations

• Incremental Updates: Process only new conversations since last run

• Full Migration: Complete conversation database rebuild

• Statistics: Comprehensive indexing and usage metrics

• Vector Migration: One-time embedding generation for existing conversations

📊 Current Scale

• Conversations: 664 processed sessions

• Messages: 118,453+ indexed messages

• Vector Chunks: 13,847 semantic chunks

• Database Size: ~420MB optimized storage

• Embedding Cost: ~$0.57 (one-time migration)

🛠️ Technical Stack

• Runtime: Node.js with TypeScript

• Database: SQLite with FTS and vector extensions

• Embeddings: OpenAI text-embedding-3-small

• Protocol: Model Context Protocol (MCP)

• Search: Hybrid semantic + keyword matching

🔒 Security Configuration

Environment Variables Setup

1. Copy the environment template:

   cp .env.example .env

2. Configure your API key:

   # Edit .env and add your OpenAI API key OPENAI_API_KEY=your_actual_api_key_here

Security Best Practices

• ✅ Environment Variables: All sensitive data is configured via environment variables

• ✅ No Hardcoded Secrets: API keys are never committed to version control

• ✅ Secure Defaults: Vector search gracefully degrades without API key

• ✅ Read-Only Access: OpenAI API is used only for text embedding generation

• ✅ Local Processing: All conversation data remains on your system

• ✅ Cost Control: Built-in token estimation and cost tracking

API Key Management

• Required For: Vector search, semantic search, AI-powered naming

• Not Required For: Traditional keyword search, conversation management

• Permissions: Read-only access to OpenAI embeddings API

• Cost: ~$0.0001 per 1,000 tokens (very low cost for typical usage)

• Rate Limits: Automatic batching and retry logic included

Data Privacy

• Local Storage: All conversation data stored locally in SQLite

• No Data Sharing: Conversations never sent to external services except for embedding generation

• User Control: Vector search entirely optional and user-controlled

• Audit Trail: All API usage logged with token counts and costs

⚡ Quick Start

Prerequisites

Build and Run

MCP Integration

Add to your Claude Code configuration:

🔍 Available Tools

Traditional Search

• search_conversations - Keyword search with role filtering

• get_recent_conversations - Latest conversations with project filtering

• get_conversation_details - Full session message history

• get_session_for_resume - Resume-formatted conversation data

Vector Search (Requires OpenAI API Key)

• vector_search_conversations - Semantic similarity search

• hybrid_search_conversations - Combined semantic + keyword search

• get_chunk_with_context - Expand search results with adjacent chunks

Management Tools

• rename_conversation - Assign custom conversation names

• generate_conversation_summary - AI-powered title generation

• list_conversations_with_names - Named conversation listing

• batch_rename_recent - Bulk conversation naming

Database Operations

• update_database - Full conversation database rebuild

• update_database_incremental - Process only new conversations

• get_indexing_stats - Database statistics and health metrics

• migrate_to_vector_database - One-time vector embedding migration

📖 Documentation

• Current Status - Real-time project state

• Architecture - System design and decisions

• API Reference - Complete tool documentation

• Development Guide - Setup and contribution guide

🎯 Performance

• Search Speed: Sub-second response for most queries

• Memory Efficient: SQLite-based storage with optimized indexes

• Scalable: Handles 100K+ messages with consistent performance

• Graceful Degradation: Traditional search works without OpenAI API key

🔧 Monitoring

Check server health:

Expected output includes traditional and vector database metrics, processing dates, and configuration status.

📝 License

Private development tool - not for redistribution.