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Snowflake MCP Agent System

by silverknight404
GitHub
Python
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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

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables intelligent data analysis and querying of Snowflake databases through specialized AI agents. Features 20+ tools for data operations, lineage tracing, usage analysis, and performance optimization with multi-agent architecture.

  1. Overview
    1. Prerequisites
      1. Installation
        1. Configuration
          1. Quick Start
            1. 1. Start the MCP Server
            2. 2. Run the Agentic Client
          2. Usage Modes
            1. Interactive Mode (Default)
            2. Single Query Mode
            3. Batch Processing Mode
            4. Training Mode
          3. Agent Archetypes
            1. Data Sources
              1. API Reference
                1. Core Tools
                2. Analysis Tools
                3. Session Management
              2. Architecture
                1. Error Handling
                  1. Development
                    1. Project Structure
                    2. Adding New Agents

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