MCP
Recent items mentioning MCP across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks Marketplace now offers ready-to-use biomedical and clinical Model Context Protocol (MCP) servers from partners, enabling easier deployment of healthcare agents 1. The dbt Semantic Layer and dbt's MCP server and agent skills are enhancing AI with essential business context for AI-ready data 5. Databricks Skill Builder also demonstrated building personalized AI agents using user-delegated actions via MCP servers, integrating Unity Catalog functions and external tools 6.
Generated daily from the 6 most recent items mentioning MCP. Click any [N] to jump to the source.
Empower your healthcare agents with ready-to-use MCP on Databricks Marketplace
Databricks Marketplace now offers ready-to-use biomedical and clinical Model Context Protocol (MCP) servers from partners like Climb and Atropos Health, empowering healthcare agents. Easily build and deploy bespoke agents to production, leveraging a securely governed, centralized MCP Catalog that also supports your own custom MCP servers or data.
Whether MCP server support for Genie is available in our workspace/region for free version.
AI-ready data in practice: What dbt Semantic Layer and dbt's MCP server and agent skills do for your team
dbt's Semantic Layer, MCP server, and agent skills now provide AI with essential business context. This enables your team to move beyond just clean data to truly AI-ready data in practice.
TutorialsMCP Servers + OBO Auth: The Formula for Context-Aware Agents
The video demonstrates how to build an AI agent in Databricks that provides personalized responses by integrating user-delegated actions through Model Context Protocol (MCP) servers. It walks through setting up Unity Catalog functions, external MCP tools like web search, and custom MCP servers to access internal APIs, all while maintaining user context for relevant information retrieval.
5 dbt MCP server patterns that work in production
Learn five dbt MCP server patterns that work in production, including one that doesn't behave as expected. These patterns are drawn from real-world production use cases.
NewsDatabricks AI Dev Toolkit: Empowering Workspace Users
The Databricks AI Dev Toolkit provides workspace users, even those unfamiliar with IDEs, access to AI tools via a Databricks app serving an MCP server. It supercharges the Genie code agent with MCP tools to automate resource creation.
NewsDatabricks Apps vs Model Serving: Authentication, Cost, and Performance Compared
Databricks Apps are now the recommended first choice for deploying agents due to their flexibility in handling full-stack applications with multiple components, offering faster iteration and local testing compared to Model Serving. Model Serving remains suitable for use cases prioritizing high QPS, governance features like AI Gateway, inference tables, and guardrails, or when scaling to zero is acceptable for cost optimization.
MLflow 3.11.1 introduces AI-powered issue detection for agent traces, budget alerts and limits for AI Gateway spending, and a new interactive graph view for visualizing trace hierarchies. It also enhances security with pickle-free model serialization and improves dependency management with native UV support.
TutorialsDatabricks AI Dev Kit Demo - Install, DataGen, SDP, Dashboard
The video demonstrates installing the Databricks AI Dev Kit on a Mac, then uses it to generate synthetic data, create serverless Spark declarative pipelines for a medallion architecture, and build a Databricks dashboard based on the generated data. It highlights how the AI Dev Kit leverages skills and an MCP server to automate these development tasks.
ReleasesIntroducing Databricks AI Dev Kit - Skills, MCP server, Builder App
The Databricks AI Dev Kit provides agent skills, an MCP server, and a Builder App to enhance AI-driven development on Databricks. It allows users to integrate AI coding tools with Databricks best practices, extending LLM capabilities through specialized functions and offering a chat-based interface for building applications.
5 Tips to Get More Out of Your Claude Code with MLflow
MLflow now offers an MCP server, CLIs, and Skills to extend Claude Code, enabling you to trace tokens and monitor tool usage. These five tips will help you transform your Claude coding agent into a transparent and controllable workflow.
NewsTurbo-Charge your Agents with instant MCP in Databricks
The video demonstrates how to use Model Context Protocol (MCP) in Databricks to give AI agents "superpowers" by enabling them to interact with various tools and data sources. It shows how to easily set up MCP servers within Databricks to connect agents to Unity Catalog functions, vector search, external APIs, and even marketplace MCP services, all without extensive coding.
NewsClaude Code: 5 Essentials for Data Engineering
The video introduces five essential concepts for using Claude Code in data engineering: the cloud.mmd file for core project information, skills for packaging expertise, commands for predefined prompts, sub-agents for focused tasks, and Model Context Protocol (MCP) for standardized tool interaction. These components help manage context and memory for effective AI-enhanced development.
NewsDatabricks: What’s new in September 2025? #databricks
Databricks now supports geospatial data types (geography and geometry) with new functions for visualization and spatial operations, and introduces serverless GPU clusters for distributed GPU code execution. The platform also offers enhanced notebook features like side-by-side editing and a notebook-specific search, along with new options for managing serverless environments, SQL warehouses, and access requests in Unity Catalog.
