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166 posts about MCP. Every summary links to the original.
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An agentic ad stack wires governed customer signals to campaign execution through a shared tool interface and server-side guardrails. The diagram shows each layer, the direction of a tool call, and where control stays human.
A four-stage flow showing how a coding agent moves from a task to a reviewed change by grounding on verified skills, live system state, and code-first config before it acts.
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Ecolab needed to combine audits, health inspections, pest telemetry, and other data from nine systems so retail teams could answer location-specific compliance questions. Its Retail Intelligence application is a native Databricks App using Lakebase Postgres, Lakeflow, and Spark Declarative Pipelines to move governed data into a Unity Catalog lakehouse, while Foundation Model APIs serve Claude Sonnet, Claude Haiku, and Gemini. A Coordinator Agent delegates requests to specialized agents that use Vector Search, SQL, Unity Catalog Functions, and an external MCP server; a Response Agent returns cited answers, with short- and long-term memory stored through Lakebase. The system also applies five Judge LLMs, MLflow tracing, and ai_query() batch inference. Report preparation fell from two weeks to under two minutes, while the assistant supports approximately twelve languages at about 98% accuracy.
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.
Fin is being made available as a Service Agent on top of HubSpot and Freshworks, allowing HubSpot and Freshdesk customers to deploy it without migrating from their helpdesk. The announcement says customers can get it integrated and live in less than an hour across voice, email, chat, social, and other channels, with a stated 76% average resolution rate. Fin is described as handling complex queries that require reading and writing to third-party systems, while remaining configurable to each business’s policies; its openness includes APIs, MCPs, a CLI, access to the Apex trained model, and public product, help-center, and developer documentation. The broader argument is that open Agent platforms let customers experiment, replace components, and avoid vendor lock-in, with competition determined by product quality.
The Cursor SDK’s TypeScript and Python releases add custom tools, auto-review, and nested subagents. Custom tools let developers pass function definitions through local.customTools, exposed via a built-in MCP server, making them available to all subagents. Auto-review routes local agent tool calls through a classifier; instructions in permissions.json control which calls run automatically and which are held for review. The SDK now supports a JSONL store for agent metadata as an alternative to SQLite, and developers can implement the LocalAgentStore interface for custom backends. Subagents can spawn their own subagents without additional configuration, and the release includes reliability and platform fixes.
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.
Anthropic announces the Services Track and Claude Partner Hub to make its partner ecosystem easier for enterprise customers to evaluate firms that can put Claude into production. The Services Track has Select, Preferred, and Global Premier tiers, measured by certified practitioners, production deployments, public customer stories, and for Global Premier, a joint business plan with named executive sponsors. Requirements include at least 10, 100, or 1,000 active certified individuals, alongside tier-specific deployment and endorsement thresholds, with a single bar for firms of all sizes. The Hub provides daily refreshed standing dashboards, a public directory showing each firm's tier and evidence, and an MCP connector that lets partners query partnership information through Claude. Promotions occur twice yearly, with an additional 2026 review, while downgrades are limited to the annual review after notice and an opportunity to close gaps.
Resend reports reaching 3,000,000 users six months after reaching 1 million, while its team grew from 25 to 43 members and weekly npm downloads increased from 1.4M to 6.8M. The company says it has been profitable since last year without reducing cloud or Datadog spending or slowing hiring, treating profitability as flexibility for product decisions rather than the goal. Its support model gives AI agents the product’s tools for routine questions while keeping engineer support available when answers are not found. For developers and agents, Resend has made every page render as Markdown, expanded MCP coverage from about 8% to about 90%, shipped a 53-command CLI, and planned remote MCP and OAuth, while positioning Automations alongside sending, receiving, and broadcasting in one email platform.
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.
Google Pay & Wallet Developer MCP server is announced to help AI assistants and IDEs complete Google Pay and Google Wallet API integrations with less context switching. It uses Model Context Protocol to connect tools including Antigravity, Cursor, and Visual Studio Code with current documentation, account details, and integration status. Its search_documentation tool applies Retrieval-Augmented Generation to return answers and code samples from official developer sites, while other tools validate Wallet pass JWTs or JSON, expose performance metrics and error trends, and manage merchant accounts and Google Pay API integrations. The stated goal is to reduce friction, improve accuracy with current documentation and real account data, and speed troubleshooting from setup through launch. A public guide and tool reference are available, with further capabilities planned based on developer feedback.
Uber describes its 2025 effort to give AI agents verifiable identities and preserve attribution as agents act for users across compositional, multi-step workflows. The problem is that conventional human and workload identities expose only generic service identities, dropping originating users and intermediate agents across hops and weakening audits, authorization, and incident response. The design extends Uber’s Zero Trust Architecture with an Agent Registry, an AI Agent Mesh, Security Token Service (STS), MCP Gateway, downstream systems, and an AI Gateway, while integrating identity and context propagation into the agent SDK. Workloads obtain cryptographically signed workload SVIDs from SPIRE, and STS issues short-lived, scoped JWTs for each hop; MCP Gateway authenticates and authorizes tool calls. Production metrics reported show P99 STS token-exchange latency consistently below 40 milliseconds, while the roadmap places dynamic access control and unified policy enforcement above the identity foundation.
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.
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 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.
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.
Notion introduces a Developer Platform with Workers, data synchronization, custom agent tools, external agents and a CLI for extending its workspace beyond built-in capabilities. As teams connect agents to more workflows, the platform addresses gaps involving external data, custom logic and agents that cannot otherwise participate in Notion. Workers run custom code in a hosted secure sandbox, enabling database sync, agent tools and webhook triggers without user-managed servers; the CLI supports authentication, Notion actions and Worker deployment from a terminal or IDE. External Agents can work natively alongside teams and Custom Agents, while the External Agent API supports bringing internally built agents into the workspace. The CLI is available on all plans, Workers are in public beta for Business and Enterprise plans, and External Agents are offered through a private-beta waitlist.
This release introduces quality-of-life improvements to the Agents Window, centered on full-screen work areas and more compact agent conversations. Full screen expands files, changes, canvases, PRs, browsers, or terminals across the right panel, replaces chat with a floating prompt bar, and is available through the panel control, command palette, or Cmd/Ctrl+Shift+M. Compact chats offer Compact, Balanced, and Detailed tool-call density settings, ranging from concise results with minimal traces to near-complete step-by-step context. The release also improves PR review visibility, scrolling, streaming, context persistence, MCP authentication, model selection, search, and task status, while fixing reliability issues across PRs, background and cloud agents, MCP/OAuth, shortcuts, hooks, model routing, webhooks, and mirrored repositories.
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.
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.
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.
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.
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