What is Mcp Chat Analysis Server

The mcp-chat-analysis-server is a Model Context Protocol (MCP) server designed for semantic analysis of chat conversations. It utilizes vector embeddings and knowledge graphs to provide tools for analyzing chat data, performing semantic searches, extracting concepts, and analyzing conversation patterns.

Use cases

The mcp-chat-analysis-server can be used in various scenarios, including customer support analysis, social media monitoring, research on conversation dynamics, and enhancing chatbots by understanding user interactions and preferences.

How to use

To use the mcp-chat-analysis-server, install the package via pip, set up the configuration file with your database settings, and run the server using Python. You can also integrate it with Claude and other MCP-compatible systems by configuring the necessary JSON settings.

Key features

- 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.

Where to use

undefined

MCP Chat Analysis Server

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.

Key Features

• 🔍 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

Quick Start

# 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

MCP Integration

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"
      }
    }
  }
}

Available Tools

import_conversations

Import and analyze chat conversations

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

semantic_search

Search conversations by semantic similarity

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

analyze_metrics

Analyze conversation metrics

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

extract_concepts

Extract and analyze concepts

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

Architecture

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

Prerequisites

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

Installation

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

Development

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/

Contributing

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

See CONTRIBUTING.md for guidelines.

License

MIT License - See LICENSE file for details.

Related Projects

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

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions