Which integrations are available for this server?

Utilizes LangGraph's multi-agent architecture to power intelligent data analysis with specialized agent archetypes including analysts, lineage experts, usage auditors, query optimizers, and metadata curators for enhanced Snowflake operations. Provides comprehensive tools for Snowflake data operations including database schema exploration, SQL query execution, usage pattern analysis, data lineage tracing, performance monitoring, and metadata management across six specialized datasets for enterprise data analytics.

How do I use Snowflake MCP Agent System?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Snowflake MCP Agent System show me the top 5 most active users in the sales database this month" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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:

AAI_USAGE: User access patterns and resource consumption
AAI_LINEAGE: Source-to-target table mappings
AAI_MD: Table metadata and data product information
AAI_PROFILER: Column-level statistics and data quality metrics
AAI_ACCESS: Role-based permissions and access control
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

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

Snowflake MCP Agent System

Snowflake MCP Agent System

Overview

Prerequisites

Installation

Configuration

Quick Start

1. Start the MCP Server

2. Run the Agentic Client

Usage Modes

Interactive Mode (Default)

Single Query Mode

Batch Processing Mode

Training Mode

Agent Archetypes

Data Sources

API Reference

Core Tools

Analysis Tools

Session Management

Architecture

Error Handling

Development

Project Structure

Adding New Agents

Resources

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