This MCP User Guide provides example prompts for common use cases.

Root Cause Analysis

The Validio MCP Server enables AI assistants to automatically trace incidents through your data lineage and identify upstream issues. The AI assistant analyzes incident groups, traces upstream dependencies, and identifies patterns across your data pipeline to pinpoint the source of data quality issues.

Example Prompt:

You: "What's causing the most recent incidents in my customer_orders table?"

Common Use Cases:

• Identifying connected incidents across multiple sources
• Understanding which upstream issues to fix first
• Prioritizing incident resolution based on downstream impact
• Documenting incident patterns for post-mortems

Setting Up Monitoring for New Sources

When you add a new data source to Validio, the AI assistant can help you quickly set up comprehensive monitoring by understanding your data schema and recommending appropriate validators.

The AI assistant examines your source schema, existing validator patterns, and data quality best practices to suggest and create validators tailored to your specific data.

Example Prompt:

You: "I just added a new Snowflake source called 'SILVER__CUSTOMERS'. Can you help me set up monitoring?"

Common Use Cases:

• Initial setup for new data sources
• Adding country/region-based segmentation automatically
• Ensuring critical fields are monitored
• Setting up monitoring standards across similar sources

Getting Intelligent Validator Recommendations

The AI assistant can analyze your existing setup and recommend validators to fill gaps in your data quality coverage.

The assistant examines your source schema, existing validators, and data characteristics to identify monitoring gaps and suggest validators that complement your current setup.

Example Prompt:

You: "Review my 'user_events' source and suggest validators to improve coverage."

Common Use Cases:

• Auditing existing monitoring coverage
• Identifying blind spots in data quality monitoring
• Standardizing monitoring across similar sources
• Improving monitoring based on best practices
• Adding segmentation to existing validators

Additional Example Prompts

The following are example prompts you can use with your AI assistant.

Incident Management

• "Summarize and prioritize incidents in the payments source"
• "What's causing incidents in my customer_orders table?"
• "Show me the incident history for the revenue validator"

Data Discovery

• "List all Snowflake assets in the catalog"
• "What validators are monitoring the users table?"
• "Show me sources with recent incidents"

Validator Configuration

• "Set up monitoring for my new sales_transactions table"
• "Create a validator to ensure no duplicate emails in users"
• "Monitor average order value with country segmentation"

Analysis & Recommendations

• "Review my user_events source and suggest improvements"
• "Which sources have the most incidents this week?"