Snowflake MCP Agent System

Enhanced MCP Snowflake server with LangGraph agentic architecture for intelligent data analysis and querying.

Overview

This system provides:

• MCP Server: 20+ specialized tools for Snowflake data operations
• Agentic Client: LangGraph-powered multi-agent system with few-shot learning
• Session Management: State persistence and intelligent caching
• Training Capabilities: Continuous improvement through user feedback

Prerequisites

• Python 3.12+
• Snowflake account with appropriate permissions
• JWT token for authentication (if using corporate endpoints)

Installation

pip install -e .

Configuration

Create a .env file with your Snowflake credentials:

# Required
SNOWFLAKE_USER=your_username
SNOWFLAKE_ACCOUNT=your_account
SNOWFLAKE_WAREHOUSE=your_warehouse
SNOWFLAKE_DATABASE=your_database
SNOWFLAKE_SCHEMA=your_schema

# Authentication (choose one)
SNOWFLAKE_PASSWORD=your_password
# OR
SNOWFLAKE_PRIVATE_KEY_PATH=path/to/private_key.pem
SNOWFLAKE_PRIVATE_KEY_PASSPHRASE=optional_passphrase

# Optional
SNOWFLAKE_ROLE=your_role
JWT_TOKEN=your_jwt_token

Quick Start

1. Start the MCP Server

# Terminal 1
python -m mcp_code server

Server runs on http://127.0.0.1:8000/mcp

2. Run the Agentic Client

# Terminal 2
python -m mcp_code --mode interactive

Usage Modes

Interactive Mode (Default)

python -m mcp_code

Chat interface with multi-agent responses and few-shot learning.

Single Query Mode

python -m mcp_code --mode query --query "What are the top 5 most used tables?"

Batch Processing Mode

python -m mcp_code --mode batch --file queries.txt

Training Mode

python -m mcp_code --mode train

Collects positive feedback examples for agent improvement.

Agent Archetypes

The system includes specialized agents:

• Analyst: EDA, statistical analysis, trend identification
• Lineage Expert: Data flow tracing, impact analysis
• Usage Auditor: Resource monitoring, anomaly detection
• Query Optimizer: Performance analysis, optimization recommendations
• Metadata Curator: Schema documentation, data cataloging

Data Sources

The system analyzes six Snowflake datasets:

1. AAI_USAGE: User access patterns and resource consumption
2. AAI_LINEAGE: Source-to-target table mappings
3. AAI_MD: Table metadata and data product information
4. AAI_PROFILER: Column-level statistics and data quality metrics
5. AAI_ACCESS: Role-based permissions and access control
6. AAI_SQL_ANALYZER: Query execution metadata and performance metrics

API Reference

Core Tools

• list_databases() - List available databases
• list_schemas(database) - List schemas in database
• list_tables(database, schema) - List tables in schema
• run_query(sql) - Execute SELECT queries

Analysis Tools

• analyze_usage(time_period, business_unit) - Usage pattern analysis
• get_lineage(table_name, direction, depth) - Data lineage tracing
• identify_heavy_users(metric, top_n) - Resource consumption analysis
• analyze_slow_queries(threshold_seconds) - Performance bottleneck identification
• get_table_metadata(table_name) - Comprehensive metadata retrieval
• recommend_data_products(analysis_scope) - Data product recommendations

Session Management

• save_feedback(session_id, query, response, feedback_type) - Training data collection
• get_session_history(session_id) - Query history and statistics

Architecture

┌─────────────────┐    HTTP/MCP    ┌──────────────────┐
│  Agentic Client │ ◄───────────► │   MCP Server     │
│  (LangGraph)    │               │  (FastMCP)       │
└─────────────────┘               └──────────────────┘
         │                                  │
         │                                  │
         ▼                                  ▼
┌─────────────────┐               ┌──────────────────┐
│  Few-Shot       │               │   Snowflake      │
│  Training Store │               │   Database       │
└─────────────────┘               └──────────────────┘

Error Handling

Common issues and solutions:

• Connection Failed: Verify Snowflake credentials in .env
• JWT Token Invalid: Update JWT_TOKEN in environment
• Import Errors: Run pip install -e . to install dependencies
• Port 8000 Busy: Server already running or port in use

Development

Project Structure

mcp_code/
├── __init__.py          # Main entry point and CLI
├── server.py            # Enhanced MCP server with tools
├── client_refactored.py # LangGraph agentic client
├── db_client.py         # Snowflake database client
├── config.py            # Configuration management
├── query_utils.py       # Query analysis utilities
└── training_examples.json # Few-shot training data

Adding New Agents

1. Create agent class inheriting from BaseAgent
2. Define _get_base_prompt() method
3. Add corresponding MCP tools in server.py
4. Update routing logic in client_refactored.py