AI Agent for Databricks MCP
Empower your AI agents to autonomously explore, understand, and query Databricks environments using the Model Context Protocol (MCP) server. Leverage comprehensive Unity Catalog metadata, advanced lineage tracing, and code-level analysis to generate precise SQL and gain actionable insights from your data ecosystem—without manual intervention.
Autonomous Data Discovery & Lineage Exploration
Let your AI agent independently explore Databricks Unity Catalog, uncovering catalogs, schemas, tables, and rich column metadata. The MCP server enables seamless context gathering, deep lineage tracing—including code, notebook, and job dependencies—and delivers actionable insights for precise and compliant SQL generation.
- Comprehensive Catalog Navigation.
- Agents can list and describe Unity Catalogs, schemas, tables, and columns, surfacing all metadata to inform query construction.
- Automated Lineage Tracing.
- Trace table, notebook, and job dependencies for complete impact analysis and robust data governance.
- Code-Level Exploration.
- AI agents can identify and analyze the actual code and business logic responsible for data transformations and quality checks.
- Semantic Metadata Access.
- Leverage detailed descriptions at every level—catalog, schema, table, and column—for greater context, clarity, and accuracy.
Intelligent SQL Query Generation
Transform your Databricks metadata into actionable insights. With rich context about your data’s structure and relationships, AI agents generate accurate, semantically correct SQL—reducing errors and accelerating analytics, all while respecting data governance and permissions.
- Execute SQL Queries.
- Agents can run arbitrary SQL against Databricks using the Databricks SDK, ideal for targeted data retrieval and analytics.
- LLM-Optimized Output.
- All descriptive tools return Markdown, optimized for LLM parsing and context gathering.
- Permission-Aware Operations.
- All queries and exploration respect Databricks Unity Catalog and SQL Warehouse permissions for secure data access.
Operationalize AI-Driven Metadata Management
Accelerate your data workflows by integrating metadata as code—manage, automate, and audit Unity Catalog assets with Terraform, while providing secure, scalable access for production AI workflows. Ensure compliance, auditability, and seamless integration with tools like Cursor and Agent Composer.
- Secure, Auditable Access.
- Leverage fine-grained permissions and token-based access for safe, compliant operations and easy audit trails.
- Infrastructure as Code.
- Manage Unity Catalog assets and metadata programmatically using Terraform for consistent, versioned deployments.
MCP INTEGRATION
Available Databricks MCP Integration Tools
The following tools are available as part of the Databricks MCP integration:
- list_uc_catalogs
Lists all available Unity Catalogs with their names, descriptions, and types for data source discovery.
- describe_uc_catalog
Provides a summary of a specific Unity Catalog, listing all its schemas with their names and descriptions.
- describe_uc_schema
Gives detailed information about a schema, including its tables and optionally their columns.
- describe_uc_table
Delivers a comprehensive description of a Unity Catalog table, including structure and lineage information.
- execute_sql_query
Executes SQL queries against the Databricks SQL warehouse, returning formatted results.
Connect Your Databricks with FlowHunt AI
Connect your Databricks to a FlowHunt AI Agent. Book a personalized demo or try FlowHunt free today!
What is Databricks
Databricks is a leading global data, analytics, and artificial intelligence (AI) company founded in 2013 by the original creators of Apache Spark. The company provides a unified analytics platform that enables organizations to seamlessly integrate data engineering, data science, machine learning, and analytics. Databricks empowers more than 10,000 organizations worldwide—including Fortune 500 companies—to manage massive volumes of data, streamline ETL processes, and accelerate the development and deployment of AI solutions. The platform is known for its collaborative workspace that bridges the gap between data engineers, data scientists, and business analysts, driving innovation and efficiency in data-driven decision making.
Capabilities
What we can do with Databricks
With Databricks, users can harness the power of unified data analytics, enabling seamless collaboration and rapid scaling of AI and machine learning projects. The platform allows organizations to integrate and process large datasets, build and deploy machine learning models, and gain actionable insights, all within a secure and collaborative environment.
- Unified Analytics
- Integrate ETL, data engineering, data science, and analytics on a single platform.
- Collaborative Workspace
- Facilitate teamwork among data engineers, scientists, and analysts with shared notebooks and tools.
- Scalable Machine Learning
- Build, train, and deploy machine learning models at scale using industry-standard frameworks.
- Data Warehousing
- Simplify data warehousing and access real-time analytics with robust data management features.
- End-to-End Security
- Ensure enterprise-grade security, governance, and compliance for sensitive data workflows.
How AI Agents Benefit from Databricks
AI agents can leverage Databricks to automate and accelerate data processing, model training, and real-time analytics. By integrating with Databricks, AI agents gain access to scalable compute resources, collaborative tools, and extensive data pipelines, enhancing their ability to generate insights, automate decisions, and deliver impactful outcomes in dynamic business environments.