---
title: "MCP"
description: "40 posts about MCP, summarised, each linking to the original."
---

# MCP
> 40 posts about MCP, summarised, each linking to the original.

## Articles

### [AWS and Databricks at Data + AI Summit 2026: Accelerating real-world AI innovation](https://yomu.fyi/post/aws-and-databricks-at-data-ai-summit-2026-accelerating-real-world-ai-i.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sarah Jack, Taylor Hoss
- Published: Jun 10, 2026

AWS and Databricks describe their expanded collaboration at Data + AI Summit 2026, where AWS returns as a Legend Sponsor with sessions, demos, customer stories, and industry forums. The partnership centers on generative AI adoption, unified governance, and open data architectures, including an agentic stack that combines Amazon Bedrock, Bedrock AgentCore, Kiro, and the Databricks Data + AI Platform. A featured integration uses a governed MCP connection through Databricks Apps so AgentCore can query Unity Catalog-governed data, ask AI/BI Genie questions, and read low-latency state from Lakebase while honoring existing permissions. AWS will demonstrate these workflows at Booth #100 and present a session on federating Unity Catalog to AWS Glue, alongside customer examples including Mastercard, Talkdesk, nCino, Addepar, and Workday. Attendees can also join technical conversations, receptions, and a 14-day Databricks on AWS Marketplace trial with $400 in usage credits.


### [Bring Databricks into Kiro IDE with the AI Dev Kit Power](https://yomu.fyi/post/bring-databricks-into-kiro-ide-with-the-ai-dev-kit-power.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Antony Prasad Thevaraj, Venkatavaradhan Viswanathan
- Published: Jun 3, 2026

AI-assisted development can produce unreliable SQL and models when an assistant guesses schema details or exceeds the user's data access. Kiro can connect to Databricks through Model Context Protocol (MCP) in two ways: four Databricks-managed remote servers for Genie, SQL, Unity Catalog Functions, and Vector Search, or the Databricks AI Dev Kit Power, which installs a local Python MCP server and broader skills. The AI Dev Kit now supports Kiro through its unified installer, while the Power provides one-click onboarding with authentication detection and skill loading. Both paths use Unity Catalog permissions, including row-, column-, and tag-based grants, so the assistant sees the user's effective access; Path A uses token-based configuration, while Path B supports OAuth U2M, OAuth M2M, profiles, or PATs. Examples show schema-grounded SQL, dbt joins using real columns, query comparisons, lineage checks, and generation of Databricks jobs or Asset Bundles.


### [Personalizing Genie Code with instructions, skills, memory, and MCP](https://yomu.fyi/post/personalizing-genie-code-with-instructions-skills-memory-and-mcp.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Samantha Banchik, Gal Oshri, Romain Rigaux, Will Tipton, Chloe Chan
- Published: Jun 1, 2026

Genie Code adds personalization through persistent instructions, task-specific Agent Skills, shared repository conventions, and MCP servers. Instructions apply across every agent-mode session, while AGENTS.md and CLAUDE.md files can be discovered automatically for team conventions. Skills are markdown-based packages with names, descriptions, guidance, reusable code, or scripts, scoped to workflows and available personally or through Workspace/.assistant/skills/. MCP servers expose external tools, data, and workflows from managed services, Unity Catalog-connected servers, or Databricks Apps, with workspace admin controls and permission-based user selection. Managed OAuth flows for Google Drive, SharePoint, and GitHub are currently in beta, while the MCP marketplace and Genie Code settings support server discovery and setup.


### [Governing AI agents at scale with Unity Catalog](https://yomu.fyi/post/governing-ai-agents-at-scale-with-unity-catalog.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi, Stefania Leone
- Published: May 20, 2026

With AI agents proliferating across organizational functions, governance must answer which agents access sensitive data and how they act. Traditional approaches either scatter logs and permissions across systems or restrict deployment, while agents require controls over autonomous tool use and runtime behavior. Unity Catalog and Unity AI Gateway address this through four pillars: delegated access, data-centric governance, cost intelligence, and open interoperability. The design passes user identity through agents with on-behalf-of tokens, evaluates MCP tool calls with Service Policies, applies inline guardrails, and records model, access, trace, and usage data in lakehouse tables. It also connects data quality, classification, spend, and outcomes, while supporting multiple frameworks and model providers through shared governance.


### [Stop rogue AI: How Unity Catalog secures your agent actions](https://yomu.fyi/post/stop-rogue-ai-how-unity-catalog-secures-your-agent-actions.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ahmed Bilal
- Published: May 19, 2026

Agentic AI systems connected to external tools can take destructive production actions while operating within delegated authority, yet without restrictions on tool selection or records of their calls. Unity Catalog now governs MCP tools with fine-grained access control, service policies, and payload logging, while Unity AI Gateway enforces decisions in real time. Policies are SQL functions receiving actor and context information; they can allow, deny, or require user consent, inspect arguments and caller properties, and block calls before execution. Every tool call is recorded in a Unity Catalog-managed Delta table with the tool name, arguments, result, user identity, and allow-or-deny status, making the activity queryable with SQL. The service policies and payload logging are available as a Gated Beta, with early access offered through Databricks account teams.


### [Databricks context engineer associate: the industry’s first certification for reliable AI agent systems](https://yomu.fyi/post/databricks-context-engineer-associate-the-industry-s-first-certificati.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Rachel Canetta, James Kantor, Trang Le
- Published: May 19, 2026

Databricks announces the Databricks Certified Context Engineer Associate, described as the industry’s first certification purpose-built for context-aware AI systems. It assesses whether practitioners can design, assemble, and govern the information AI agents receive at inference time, including system prompts, retrieval, memory, external tools, context-window management, and governance. Candidates work with Databricks technologies such as AI Search, Lakebase, MLflow, MCP, and Unity Catalog, while advanced coverage includes multi-agent systems, long-horizon workflows, and evaluation of context decisions. The beta exam will be available free to Data + AI Summit attendees, who may take it once; beta results are expected in 6–8 weeks.


### [What’s new in Unity AI Gateway: service policies, guardrails, observability, and cost controls for AI agents and MCPs](https://yomu.fyi/post/what-s-new-in-unity-ai-gateway-service-policies-guardrails-observabili.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi, Kelly Albano
- Published: May 19, 2026

Unity AI Gateway is expanding runtime governance for production AI agents, model calls, and MCP tool interactions as teams face rising costs, unclear behavior, and limited control. The Beta adds LLM-based guardrails, token-level cost attribution with per-user alerts and hard budget limits, payload logging, and MCP service policies. Guardrails use a model and prompt to evaluate inputs, outputs, or both in real time, while inference tables and Unity Catalog system tables centralize governed records of usage and interactions. For MCPs, administrators can define SQL policies as Unity Catalog functions using agent identity, user context, and request parameters to constrain tool access and sensitive actions. The capabilities are available in Beta, with payload logging and service policies offered through gated enrollment, and are intended to improve observability, compliance, and cost control.


### [MCP Marketplace brings real-time intelligence to agentic applications](https://yomu.fyi/post/mcp-marketplace-brings-real-time-intelligence-to-agentic-applications.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Roman Ostrovski, Harish Gaur, Antoine Amend
- Published: May 8, 2026

The MCP Marketplace connects agentic applications with real-time external intelligence alongside enterprise data. The problem appears in use cases such as loan approval, where historical internal records omit market conditions, updated credit signals, property changes, and competitor activity, making manual research a bottleneck. Databricks Marketplace provides governed access to MCP servers from You.com, Moody’s, and Cotality, while Unity Catalog authenticates connections and tracks access and lineage; Lakebase stores state, decisions, and audit trails across multi-step workflows. Examples show agents combining internal data with web research, credit ratings and sector outlooks, or property-resolution and mortgage signals before surfacing decisions for human review, including a commercial-loan flow with recorded sources, timestamps, and approver.


### [Operationalizing AI for public sector fraud prevention](https://yomu.fyi/post/operationalizing-ai-for-public-sector-fraud-prevention.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Johnathan Tafoya, Kacey Hertan
- Published: Apr 28, 2026

Public-sector agencies face fraud methods including synthetic identities, deepfake-enhanced documentation, and personalized social engineering, while legacy controls remain fragmented and difficult to scale. The fictional Services Bureau demonstrates an operating model that combines Databricks Apps, Lakebase, Unity Catalog, Delta Sharing, Agent Bricks, and AI/BI Genie in a single fraud-operations environment. Governed data lands in Delta tables; Unity Catalog applies attribute-based access control, masks PII by role, and provides lineage, while agents connect live lakehouse queries, agency policies, and external fraud signals through MCP. Analysts review evidence and recommendations, then approve, override, or escalate cases, keeping human judgment central. The described workflow turns weeks of manual investigation into a day, supports dashboards and conversational SQL, and is presented as making fraud decisions faster, more secure, transparent, and defensible.


### [From months to minutes: Building real-time clinical data pipelines with natural language](https://yomu.fyi/post/from-months-to-minutes-building-real-time-clinical-data-pipelines-with.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Matthew Giglia, Tim Kessler, Assunta Carey-Saylor
- Published: Apr 28, 2026

Healthcare data teams often spend months integrating EHR systems, normalizing HL7, CCD, and X12, and routing data through intermediary storage before analytics, creating latency and maintenance burden. Databricks and Redox describe a pipeline model that combines Redox MCP Server, natural-language prompts, and Databricks Zerobus Ingest to build integrations inside Databricks and stream clinical data directly into Unity Catalog managed tables. The post says Zerobus provides subsecond latency, while the MCP Server identifies environments, suggests workflow steps, executes integration tasks, and surfaces validation signals such as logs and performance summaries. A demonstrated workflow retrieved a recent patient admission as structured data with a plain-language summary, and Redox writeback can return AI outputs to EHRs for point-of-care action. The same foundation is presented as enabling real-time use cases and Redox Agents built with Databricks Genie Spaces.


### [Agentic data engineering with Genie Code and Lakeflow](https://yomu.fyi/post/agentic-data-engineering-with-genie-code-and-lakeflow.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Gal Oshri, Camiel Steenstra, Lennart Kats, Joanna Zouhour
- Published: Apr 28, 2026

Genie Code is presented as a natural-language assistant for developing, orchestrating, deploying, and debugging data pipelines and jobs. It uses pipeline and job context, including code, configuration, run results, Unity Catalog metadata, lineage, popularity, and code samples, to help engineers discover datasets and understand data flows. Engineers can describe pipelines or jobs, and Genie Code can generate Spark Declarative Pipelines with Bronze, Silver, and Gold layers, sources, transformations, data quality expectations, and outputs, then configure orchestration, schedules, dependencies, Auto Loader, AutoCDC flows, and Declarative Automation Bundles. It also analyzes failures and unexpected row-count or schema changes, proposes cross-file updates with reviewable diffs, and supports extensions through custom instructions, agent skills, and MCP servers. The stated result is faster development and guided debugging while workflows remain aligned with Unity Catalog governance, performance, and data quality standards; future plans include background failure response and cluster right-sizing.


### [The next generation of Databricks Genie](https://yomu.fyi/post/the-next-generation-of-databricks-genie.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ken Wong, Dillon Morrison, Richard Tomlinson
- Published: Apr 26, 2026

The next generation of Databricks Genie expands the assistant beyond individual Genie Spaces, combining structured and unstructured enterprise data in a unified chat experience. It reuses logic from certified Genie Spaces, governed dashboards, and Databricks Apps, with metadata-based routing that prioritizes higher-trust sources, while new reasoning models and agent architecture handle questions spanning multiple domains. Built-in connectors for Google Drive and SharePoint, plus MCP support, let Genie access knowledge stores and take actions; Unity Catalog AI Gateway manages these connections. The experience replaces Databricks One as an account-level global home, adding domains, custom URLs, unified login, automated identity management, and governance through Unity Catalog. Native iOS and Android apps extend access to dashboards, apps, and chat beyond desktop environments.


### [OpenAI GPT-5.5 + Codex, now available and fully governed on Databricks](https://yomu.fyi/post/openai-gpt-5-5-codex-now-available-and-fully-governed-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Patrick Wendell, Hanlin Tang, Ahmed Bilal, Arnav Singhvi, Ivan Zhou, Harish Gaur
- Published: Apr 24, 2026

Databricks announces native support for OpenAI’s GPT-5.5, making it available for coding workflows with Codex, enterprise agents, document pipelines, and data-driven employee workflows. Access is governed through Unity AI Gateway, which provides permissions and rate limits, configurable guardrails for PII, prompt injection, and content safety, MCP tool-call auditing, failover, and request-level observability for model and Codex interactions. The announcement describes GPT-5.5 use with Genie for natural-language analytics, Agent Bricks Custom Agents for multi-step workflows, and Lakeflow Spark Declarative Pipelines for document-focused GenAI ETL involving summarization, extraction, and classification. GPT-5.5 is available on Databricks across AWS, Azure, and GCP, with entry points including AI Playground, governed Codex endpoints, Agent Bricks, and the Foundation Model API.


### [How to transform document activation workflows with Genie and Agent Bricks](https://yomu.fyi/post/how-to-transform-document-activation-workflows-with-genie-and-agent-br.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Elena Tesser
- Published: Apr 22, 2026

Enterprise document workflows often require people to read PDFs, copy fields into spreadsheets, and re-enter data into business systems, creating delays, error risk, and weak auditability. The proposed alternative combines AI/BI Genie for natural-language queries over Delta tables, Agent Bricks for production agents, and Unity Catalog for permissions, lineage, and traceability. A five-phase workflow extracts fields into Lakeflow Bronze, Silver, and Gold layers, then supports structured queries, clause-level RAG answers, multi-agent routing, and actions through MCP servers. Examples include syncing validated rights data to ERP or CRM systems, updating billing entitlements, and triggering workflows from detected obligations. The approach targets media, advertising, ad tech, and telecommunications, where customers see improvements including faster month-end close, recovered revenue, reduced leakage, and lower operational risk.


### [LLM apps: Real-world use cases, core building blocks and risks to know](https://yomu.fyi/post/llm-apps-real-world-use-cases-core-building-blocks-and-risks-to-know.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 21, 2026

LLM applications are presented as production systems that extend beyond chatbots by connecting foundation models to external data, tools, APIs, memory and workflows. Modern categories include assistants and copilots, retrieval-augmented generation (RAG), autonomous agents, multi-agent systems and local or on-device inference, each with distinct capabilities and trade-offs. A typical RAG pipeline uses vector search over a curated knowledge base, then supplies retrieved passages to the model; chunking, source freshness and retrieval accuracy affect results. Production systems also require orchestration, evaluation, observability, governance, guardrails, audit trails and human-in-the-loop checkpoints, especially when agents can call tools or execute workflows. The source distinguishes prompt engineering, which guides behavior without changing model weights, from fine-tuning, which adapts models to domain-specific tasks, and describes a unified path combining data, models, retrieval, orchestration, evaluation and governance for production deployment.


### [Bridging data science and marketing: Databricks unveils Delta Sharing integration for Adobe Experience Platform and agentic marketing workflows](https://yomu.fyi/post/bridging-data-science-and-marketing-databricks-unveils-delta-sharing-i.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Justin Fenton, Dan Zuckerberg, Katy Yuan
- Published: Apr 20, 2026

Databricks and Adobe announce three partnership milestones aimed at connecting governed Lakehouse data with marketing activation in Adobe Experience Platform. The changes include Delta Sharing support for zero-copy access, a Databricks Genie connection to Adobe Marketing Agent through Model Context Protocol (MCP), and an upcoming Adobe Marketing Agent beta for the Databricks Marketplace. Using Unity Catalog as the source, Experience Platform can access shared tables and views as virtual tables, while Adobe Data Distiller queries live Databricks data without copying underlying records. Through MCP, agents in both platforms can use governed datasets, metadata, models, campaign metrics, SQL Warehouses, and Model Serving endpoints with natural-language interactions. The stated outcome is a shared, real-time context for audience creation, campaign analysis, closed-loop attribution, and model improvement, while reducing data movement, duplication, latency, egress, and governance overhead.


### [Governing coding agent sprawl with Unity AI Gateway](https://yomu.fyi/post/governing-coding-agent-sprawl-with-unity-ai-gateway.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aarushi Shah, Ankit Mathur, Bilal, Kevin Stumpf, Rohit Agrawal, Harish Gaur, Ana Nieto
- Published: Apr 17, 2026

Databricks introduces coding agent support in Unity AI Gateway to address security, cost, and visibility challenges created by organizations using multiple coding tools. The gateway provides a unified governance hub for Codex, Cursor, and Gemini CLI, combining access controls, usage statistics, operational observability, cost management, guardrails, and inference capacity. Agent access can be governed centrally, with audit logs in Unity Catalog, MCP servers managed in Databricks, MLflow tracing, shared cost limits, and coding metrics and traces ingested into Unity Catalog-managed Delta tables through OpenTelemetry. The announcement says developers can authenticate with Databricks credentials across connected services, use centralized budgets and model capacity, and let organizations analyze adoption, developer velocity, and rate-limit pressure in the lakehouse; support for Cursor, Gemini CLI, and Codex CLI is available to all Databricks customers.


### [Unity AI Gateway: How to connect agents to external MCPs securely](https://yomu.fyi/post/unity-ai-gateway-how-to-connect-agents-to-external-mcps-securely.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ahmed Bilal, Sunish Sheth
- Published: Apr 15, 2026

Databricks Unity AI Gateway provides a governed way to connect agents to external Model Context Protocol (MCP) servers while integrating management with Unity Catalog. The post addresses provider-specific OAuth app registration, client secrets, token refresh, permission auditing, and the lack of centralized visibility into agent access. It describes registering an external MCP server, choosing per-user OAuth or a shared principal, selecting GitHub, and letting Databricks handle OAuth registration, token exchange, and refresh; connections can then be tested in AI Playground or with DatabricksMCPClient and deployed through Agent Bricks. Unity Catalog supplies permissions and audit records, while MLflow Tracing captures requests and tool interactions, and supported providers include Glean, GitHub, Atlassian, Google Drive, and SharePoint across AWS, Azure, and GCP.


### [Expanding agent governance with Unity AI Gateway](https://yomu.fyi/post/expanding-agent-governance-with-unity-ai-gateway.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi
- Published: Apr 15, 2026

Unity AI Gateway extends Unity Catalog’s permissions, auditing, and policy controls to agentic workflows involving LLMs, MCP servers, and APIs. The release addresses limited visibility across multi-step actions with on-behalf-of user execution for MCP calls, configurable guardrails, and consistent governance across model providers without separate configurations for each provider and model choice can vary by task. Guardrails can detect PII, prompt injection, unsafe content, and data exfiltration, while logs capture identities, timestamps, MCP details, dollar costs, payloads, latency, status codes, and errors. OpenAI-compatible APIs, fallback models, and endpoint-, user-, or group-level rate limits support production operation, although some capabilities are in Beta or rolling out and availability is limited to supported Databricks regions.


### [Building Ramp's MCP server](https://yomu.fyi/post/building-ramp-s-mcp-server.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Junho Choi
- Published: Mar 25, 2025

Ramp built an open-source MCP server that exposes its developer API to LLMs through tools and a SQL interface, enabling natural-language access to business spend data. Using FastMCP in Python and Claude Desktop, the prototype could run analyses, generate visualizations, and issue cards on demand, but pagination struggled beyond a few hundred transactions because of miscalculations, context limits, input sizes, and token use. The team replaced bulk responses with an in-memory ETL that flattens API JSON into SQLite, then exposes load, transformation, and query tools; an OLAP-powered API was added for reporting workloads that caused timeouts. This let Claude analyze tens of thousands of spend events with aggregate and window functions while reducing token usage, although API latency, occasional reliability problems, tool-selection errors, and security risks remain; audit logs, OAuth scopes, and constrained tools provide mitigations, while write actions await a safety framework.


[Newer posts](https://yomu.fyi/topic/mcp.md)