A Model Context Protocol (MCP) server that enables semantic analysis of chat conversations through vector embeddings and knowledge graphs. This server provides tools for analyzing chat data, performing semantic search, extracting concepts, and analyzing conversation patterns.

• 🔍 Semantic Search: Find relevant messages and conversations using vector similarity
• 🕸️ Knowledge Graph: Navigate relationships between messages, concepts, and topics
• 📊 Conversation Analytics: Analyze patterns, metrics, and conversation dynamics
• 🔄 Flexible Import: Support for various chat export formats
• 🚀 MCP Integration: Easy integration with Claude and other MCP-compatible systems

# Install the package
pip install mcp-chat-analysis-server

# Set up configuration
cp config.example.yml config.yml
# Edit config.yml with your database settings

# Run the server
python -m mcp_chat_analysis.server

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "chat-analysis": {
      "command": "python",
      "args": ["-m", "mcp_chat_analysis.server"],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "NEO4J_URL": "bolt://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "your-password"
      }
    }
  }
}

Import and analyze chat conversations

{
    "source_path": "/path/to/export.zip",
    "format": "openai_native"  # or html, markdown, json
}

Search conversations by semantic similarity

{
    "query": "machine learning applications",
    "limit": 10,
    "min_score": 0.7
}

Analyze conversation metrics

{
    "conversation_id": "conv-123",
    "metrics": [
        "message_frequency",
        "response_times",
        "topic_diversity"
    ]
}

Extract and analyze concepts

{
    "conversation_id": "conv-123",
    "min_relevance": 0.5,
    "max_concepts": 10
}

See ARCHITECTURE.md for detailed diagrams and documentation of:

• System components and interactions
• Data flow and processing pipeline
• Storage schema and vector operations
• Tool integration mechanism

• Python 3.8+
• Neo4j database for knowledge graph storage
• Qdrant vector database for semantic search
• sentence-transformers for embeddings

1. Install the package:

pip install mcp-chat-analysis-server

2. Set up databases:

# Using Docker (recommended)
docker compose up -d

3. Configure the server:

cp .env.example .env
# Edit .env with your settings

1. Clone the repository:

git clone https://github.com/rebots-online/mcp-chat-analysis-server.git
cd mcp-chat-analysis-server

2. Install development dependencies:

pip install -e ".[dev]"

3. Run tests:

pytest tests/

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

See CONTRIBUTING.md for guidelines.

MIT License - See LICENSE file for details.

• Model Context Protocol (MCP)
• Claude Desktop
• Qdrant Vector Database
• Neo4j Graph Database

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions