---
title: "MCP"
description: "167 posts about MCP, summarised, each linking to the original."
---

# MCP
> 167 posts about MCP, summarised, each linking to the original.

## Articles

### [How the FDA Built an AI Platform That 85% of Its Staff Now Use Daily](https://yomu.fyi/post/how-the-fda-built-an-ai-platform-that-85-of-its-staff-now-use-daily.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Molly Just-Behr
- Published: Jul 23, 2026
- Type: Explainer

The FDA built ELSA, a generative AI platform for its 16,000 staff, on Halo, a governed Databricks data foundation created to address fragmented systems across eight centers. Those centers had separate chatbots and data stores; consolidating 50 to 60 sources enabled faster sharing, real-time streaming, and centralized access controls through Unity Catalog. Within roughly two months, ELSA adoption rose from less than 1% to 85%, while staff began building hundreds of agents weekly from standard operating procedures, regulatory guidance, and center-specific documents. MCP servers layered over Unity Catalog make governed data and tooling accessible beyond data scientists, and Databricks ML and NLP capabilities through MLflow extracted starting materials and product-supplier-manufacturer relationships from millions of submission pages. A reviewer can now request grounded starting-material information for a drug application in about three minutes instead of days, while the FDA adapts center-specific MCP tools and extends the model across its organization.


### [Provisioning for the Agentic Era: How Databricks Built a Self-Serve Infrastructure Vending Machine](https://yomu.fyi/post/provisioning-for-the-agentic-era-how-databricks-built-a-self-serve-inf.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Evan Pandya, Greg Wood, Joel Thomas
- Published: Jul 23, 2026
- Type: Problem & solution

Databricks built the Field Engineering Vending Machine (FEVM) to replace shared environments with isolated, centrally governed infrastructure for its growing field organization. Users or agents describe a use case and configuration details, receiving an environment assembled through a React frontend, Python backend, Databricks Apps, Terraform, Git Runner, and Lakebase across AWS, Azure, and GCP. FEVM supports templates and add-ons such as Lakebase, notebooks, and pre-packaged assets from UC Volumes, applies configurable time-to-live policies, and sends Slack notifications for provisioning, expiration, and deletion. It also manages shared resources independently, including catalogs that persist across workspace lifecycles, and provides administrative controls for limits and audits. At the time described, it managed more than 2,600 active deployments across three clouds and over 5,000 active users, after handling nearly 1,200 requests during one internal event.


### [Why R&D Data Belongs in the Lakehouse - and Why Agents Need It There](https://yomu.fyi/post/why-r-d-data-belongs-in-the-lakehouse-and-why-agents-need-it-there.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sebastian Eberhardt, Dominik Bentele, Jonathan Bräuer
- Published: Jul 21, 2026
- Type: Explainer

cellcentric describes how it built a governed lakehouse foundation for R&D data so industrial AI agents can reason over engineering context, not isolated source-system records. On Azure and Databricks, the Data Hub combines Unity Catalog governance, Lakehouse Federation, Delta Sharing, Declarative Automation Bundles, and an MCP interface for agents. Its Fuel Cell Passport unifies five enterprise systems, models seven hierarchy levels, refreshes daily, and uses a state-based temporal model for point-in-time configuration and rework-history questions. The platform treats context coverage as a data-quality metric, with 27 published products averaging 90% column-comment coverage, while authenticated user identity and Unity Catalog authorization constrain agent access. For many R&D and process-development workflows, work that once took weeks now ships in days, though complex multi-source investigations still require substantial domain review.


### [How We Made All Swiggy Apps Operations AI-Native](https://yomu.fyi/post/how-we-made-all-swiggy-apps-operations-ai-native.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Raj Gohil
- Published: Jul 17, 2026
- Type: Explainer

Swiggy’s mobile engineering teams faced operational overload as releases, production signals, and reviews required switching among Jira, Confluence, Bitrise, Play Console, Crashlytics, New Relic, Google Sheets, ImageKit, and Slack. The response was an operating layer built around Slack-native agent workflows, reusable AI skills, MCPs, and scheduled jobs connected to release, observability, QA, cost, and internal mobile systems. It can generate readiness updates, group Jira work, identify missing owners and blockers, summarize vitals, analyze alerts, follow up TRM actions, and recommend RAMP, HOLD, or ROLLBACK for staged rollouts by correlating Crashlytics, Play Vitals, and adoption. Humans still approve rollout actions, while recurring channel reports make observability and accountability more consistent and reduce clerical integration work. The stated model lets agents handle mechanical load while engineers retain judgement, ownership, product quality, architecture, and execution.


### [Evolving Spec-Driven Development: Conductor Now Supports Antigravity](https://yomu.fyi/post/evolving-spec-driven-development-conductor-now-supports-antigravity.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Mahima Shanware, Sherzat Aitbayev, Jay Kornder
- Published: Jul 16, 2026
- Type: Announcement

Conductor, introduced as a Gemini CLI extension for spec-driven development (SDD), is evolving into a plugin that supports Antigravity CLI and other tools. The change moves project awareness from ephemeral chat logs into persistent, version-controlled Markdown artifacts while replacing strict command sequences with conversational creation and iteration of context, specs, and plans. Plugins package skills, rules, MCP servers, and hooks, and the Conductor Plugin can update project context and mark plan tasks complete as discussions progress, while preserving existing commands, specs, plans, shared configuration, and development tracks. The post reports a higher success rate on the most complex subset of TerminalBench tasks than a user not using SDD, and gives an Antigravity installation command: agy plugins install https://github.com/gemini-cli-extensions/conductor.


### [Agents for financial services](https://yomu.fyi/post/agents-for-financial-services.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 15, 2026
- Type: Announcement

Anthropic is releasing ten ready-to-run agent templates for financial services, targeting work such as pitchbook creation, KYC screening, and month-end close operations. Each template packages task-specific skills, governed data connectors, and subagents, and is available as a plugin for Claude Cowork or Claude Code, or as a cookbook for Claude Managed Agents. Claude add-ins for Excel, PowerPoint, Word, and, soon, Outlook carry context between applications, while new connectors and a Moody’s MCP app provide access to market, research, credit, and company data. The announcement says the updates pair best with Claude Opus 4.7, which leads Vals AI’s Finance Agent benchmark at 64.37%, and states that templates are available today through the financial services marketplace.


### [Claude for Creative Work](https://yomu.fyi/post/claude-for-creative-work.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 13, 2026
- Type: Announcement

Claude for Creative Work describes a set of connectors and workflows intended to help creative professionals expand ideation, reduce repetitive work, and take on larger-scale projects. The connectors connect Claude with tools including Ableton, Adobe Creative Cloud, Affinity by Canva, Autodesk Fusion, Blender, Resolume, SketchUp, and Splice, supporting documentation access, asset production, 3D modeling, live visual control, and sample search. The described uses also include tutoring, Claude Code scripts and plugins, cross-application pipeline work, Claude Design exploration with export to Canva, and batch processing. Blender has an officially available MCP connector that can analyze and debug scenes, batch-apply object changes, and add tools through Blender’s Python API; because it uses MCP, it is accessible to other LLMs. The post also describes access for students and faculty in three art and design programs, whose feedback will inform understanding of creative practitioners’ needs.


### [Ask, build, compose: What our 5th Genie Hackathon taught us about Databricks Genie](https://yomu.fyi/post/ask-build-compose-what-our-5th-genie-hackathon-taught-us-about-databri.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Shruti Prasanna, Rob Bajra
- Published: Jul 9, 2026
- Type: Explainer

The fifth Databricks hackathon used Databricks Genie to test three ways of working with governed data: asking, building, and composing. Genie Agents give business users domain-specific natural-language access to curated data, while Genie Code helps analysts create metric views, functions, pipelines, and dashboards inside Databricks. The composition track uses Conversation APIs and a managed MCP server to make Genie an agent tool alongside custom logic, Model Serving, and other MCP servers, with Unity Catalog governing access. Projects included supervisory routing across 190 tables, governance and analytics products built in days, and multi-agent systems such as ShipBob’s overnight operations brief. Across the tracks, the stated lesson is that shared governance and semantic context let business users, builders, and engineers use Genie at different levels without abandoning grounded, permissioned data access.


### [Sunsetting new.email](https://yomu.fyi/post/sunsetting-new-email.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Jul 8, 2026
- Type: Announcement

Resend is sunsetting new.email, which will remain available for template exports until August 7, 2026, before stopping new prompts and sends. Launched in February 2025, the tool let anyone create email templates with natural language, using React Email components and deliverability lessons from sending millions of emails daily; its goal was consistent, beautiful, responsive email. Resend says the core bet was right, but a separate brand and stack—repository, infrastructure, deployment, and operational conventions—made the product harder to maintain, while market feedback showed users did not need new templates constantly. The product’s ideas are moving into the Resend editor, which offers URL-based brand extraction, AI block rewriting, LLM content review, and email creation and editing through the Resend MCP server.


### [OpenAI and Databricks at DAIS 2026: Making enterprise AI real](https://yomu.fyi/post/openai-and-databricks-at-dais-2026-making-enterprise-ai-real.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Margaret Amori
- Published: Jul 6, 2026
- Type: Announcement

At Data + AI Summit 2026, Databricks and OpenAI presented a partnership centered on combining OpenAI’s frontier models and agents with Databricks’ enterprise context and control. The post describes GPT models and Codex running natively on Databricks, with Unity AI Gateway governing interactions, budgets, routing, auditing, and visibility, while Agent Tools provide governed enterprise data access through MCPs. Examples include OpenAI’s marketing data foundation on Databricks, which cut storage costs by $400,000 per month, and a Hertz application built in 11 business days with GPT-5.5 and Databricks that lifted lead conversion from 60–65% to 75–80%. The sessions argue that deployment, security, evaluation, monitoring, context, and sharing comprise most of the work around enterprise agents. A joint virtual event scheduled for August will address shipping agentic applications at scale, including a Stellantis case study.


### [How Notion used the Cursor SDK to embed coding agents](https://yomu.fyi/post/how-notion-used-the-cursor-sdk-to-embed-coding-agents.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Roshan Sadanani
- Published: Jun 25, 2026
- Type: Announcement

Notion integrated Cursor coding agents into its product so users can delegate work by tagging Cursor in a document, mentioning it in a thread, or assigning a database issue. Built with the Cursor SDK in a couple of weeks, the integration lets agents plan, build, test, verify work, and open pull requests while Notion focuses on product and user experience rather than agent infrastructure. Notion placed Cursor behind a provider-agnostic harness: a Notion thread maps to an agent, each message starts a run, and the first run can specify the prompt, repository, model, MCP servers, and automatic PR creation. Follow-up runs stream over SSE, support resumption after dropped connections, and use remote MCP to let Cursor read and write workspace state in real time; templates, custom instructions, selected tools, skills, subagents, and triggers provide further customization.


### [Genesis Workbench: A blueprint for industry AI in life sciences, powered by Databricks and NVIDIA](https://yomu.fyi/post/genesis-workbench-a-blueprint-for-industry-ai-in-life-sciences-powered.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mark Lee, Srijit Nair
- Published: Jun 23, 2026
- Type: Explainer

Genesis Workbench is an open blueprint for a Databricks-native life-sciences application that brings computational drug-discovery stages into one governed workbench. It combines Unity Catalog governance, MLflow tracking, Model Serving, serverless GPU compute, Databricks AI Search, and NVIDIA technologies including CUDA-X libraries, Parabricks, BioNeMo tools, GenMol, and Proteina-Complexa. Independent modules cover genomics, single-cell analysis, large- and small-molecule workflows, and model fine-tuning, with handoffs spanning gene-to-sequence resolution, structure prediction, docking, ADMET, and candidate ranking. A point-and-click React interface supports bench scientists, while declarative workflow generation and MCP exposure let pipelines and external clients use the workbench; inference runs on GPU endpoints inside the governed workspace without runtime external API dependencies. The stated aim is to let teams move from disease hypotheses to ranked therapeutic candidates on their own data, with a roadmap for automated workflow generation, BioNeMo Skills integration, and additional MCP services.


### [Guide to Agentic Systems and AI Agents](https://yomu.fyi/post/guide-to-agentic-systems-and-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026
- Type: Explainer

Agentic AI systems are goal-directed software platforms that perceive context, plan and execute multi-step workflows, and adapt based on outcomes with minimal human intervention. The guide distinguishes them from traditional and generative AI, defining agents, broader system architecture, and the role of LLMs as reasoning cores connected to memory, APIs, databases, and other tools. It describes a perceive-reason-act-learn loop, multi-step planning, external tool integration through interfaces such as the Model Context Protocol (MCP), and orchestration patterns for coordinating specialized agents. Production concerns include retries, queues, observability, permissions, privacy, logging, and human escalation, while stated risks include reward-hacking, unintended actions, and explainability gaps. It identifies repetitive, data-rich workflows with clear success criteria and bounded error consequences as the best current enterprise candidates.


### [Scaling out Distroless adoption With AI](https://yomu.fyi/post/scaling-out-distroless-adoption-with-ai.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jia Yee Chong
- Published: Jun 22, 2026
- Type: Problem & solution

Grab is migrating from heavy base images to Distroless images to reduce security risks, despite runtime failures caused by missing shared objects, utilities, or expected directory structures. Its medium tests run services in Docker, leave internal dependencies unmocked through Testcontainers, mock external dependencies with a network proxy, and execute in CI to catch Dockerfile regressions before deployment. For services without this setup, an agentic workflow detects candidates, scaffolds tests, fixes repository-specific failures, raises GitLab merge requests, and monitors CI while processing batches in parallel. Claude Code uses MCP integrations for GitLab, Glean, and Sourcegraph, with guardrails limiting changes to test files and CI configuration and humans reviewing every draft merge request. In roughly 1.5 months, the agent raised more than 100 medium-test merge requests and reduced work for 400 services from 400–1,200 engineer-days to approximately 0.1 days per service; Distroless adoption also grew substantially.


### [Kimi K2.7 Code vs Claude Fable 5: Landing pages that cost 94% less](https://yomu.fyi/post/kimi-k2-7-code-vs-claude-fable-5-landing-pages-that-cost-94-less.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jun 17, 2026
- Type: Benchmark

The experiment compares Kimi K2.7 Code and Claude Fable 5 across 12 landing-page prompts covering B2B SaaS, a rooftop speakeasy, developer tools, and other categories. Kimi cost about 94% less overall, averaging roughly 16 times cheaper than Fable, while producing similar-quality output, especially when given visual references. The team first established a prompt-only baseline, then used a custom design MCP server to provide screenshots, UI elements, and other references directly to multimodal Kimi; this improved hierarchy, typography, composition, readability, loading behavior, and image handling. GPT-5.5 scored screenshots and source code on a 0–100 rubric, and although Fable scored higher, the relatively small quality gap led the authors to consider Kimi’s lower cost a reasonable trade-off for iterative landing-page generation.


### [Announcing the Agentic Resource Discovery specification](https://yomu.fyi/post/announcing-the-agentic-resource-discovery-specification.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Junjie Bu, Srinivas Krishnan
- Published: Jun 17, 2026
- Type: Announcement

Agentic Resource Discovery (ARD) is an open specification for publishing, discovering, and verifying AI capabilities across the web, addressing the lack of a standard that works across organizations and ecosystems. It uses organization-hosted catalogs, including an ai-catalog.json file at a well-known domain path, plus federated registries that crawl, index, and return capabilities with trust metadata. Clients can query a registry with plain-language intent or fetch a known catalog directly, verify the publisher’s cryptographic identity, then connect at runtime through the capability’s native protocol or API. Google Cloud is bringing ARD to Gemini Enterprise Agent Platform through Agent Registry, which supports hosted discovery and governance features including namespaced URNs, egress policies, specification pinning, and Agent Identity trust-manifest verification. The specification is available under Apache 2.0, with native Agent Platform support planned in the coming months.


### [A2UI + MCP Apps: Combining the best of declarative and custom agentic UIs](https://yomu.fyi/post/a2ui-mcp-apps-combining-the-best-of-declarative-and-custom-agentic-uis.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Google A2UI Team, Ido Salomon, Liad Yosef
- Published: Jun 17, 2026
- Type: Explainer

Agentic UIs must balance deep customization with seamless integration: MCP Apps provide standard-web flexibility in an iframe, while A2UI renders declarative JSON through the host application's native components. The post presents three architectural patterns for combining them, including A2UI over MCP, where MCP servers return application/a2ui+json resources for native rendering and can separate static MCP Resources from dynamic Tool calls. This arrangement avoids iframe-related visual fragmentation, keeps backend tool access separate from frontend rendering, and supports rendering across React, Flutter, or Angular through a predefined component catalog. The broader combination also reserves MCP Apps for highly tailored experiences and describes A2UI in MCP Apps, where an embedded app can process A2UI responses and route user actions through an AppBridge; the conclusion recommends choosing among patterns according to project constraints.


### [Agent Bricks: Data + AI Summit 2026](https://yomu.fyi/post/agent-bricks-data-ai-summit-2026.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Tang, Kasey Uhlenhuth, Akhil Gupta, Patrick Wendell
- Published: Jun 16, 2026
- Type: Announcement

At Data + AI Summit 2026, Databricks announced Agent Bricks as a comprehensive developer platform for building and operating agents, extending a product launched the previous year. The announcement frames the core agent loop as only 1% of the work, with token capacity, deployment, security, evaluation, monitoring, context, and sharing forming the remaining infrastructure burden. Agent Bricks addresses choice, context, and control through support for multiple proprietary, open-source, and custom models, any agent harness, MCP-connected data, Genie Ontology, managed memory, document intelligence, sandboxes, and governed tools. Unity AI Gateway adds catalogs, fine-grained access controls, budgets, traffic routing, contextual policies, monitoring, and registry support for agents, tools, and models. Databricks says more than 100,000 agents have been built and customers including AstraZeneca, 7-Eleven, Fox Corporation, and Block have shipped agents on the platform.


### [AI governance at Data + AI Summit 2026: What’s new with Unity AI Gateway](https://yomu.fyi/post/ai-governance-at-data-ai-summit-2026-what-s-new-with-unity-ai-gateway.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi, Stefania Leone, Ahmed Bilal, Kevin Stumpf, Martin Grund, Vladimir Kolovski, Kelly Albano
- Published: Jun 16, 2026
- Type: Announcement

Databricks announces new Unity AI Gateway capabilities for governing enterprise AI as organizations operate multi-model, multi-agent, and multi-vendor estates connected to models, MCP services, APIs, and tools. The update adds unified spend visibility, granular attribution, hard spend caps, and smart routing, alongside Unity Catalog support for registering and governing models, MCP services, agents, and skills. Contextual Service Policies, in Beta, can allow, deny, or require approval for actions based on users, agents, models, tools, services, or request and response contents, with guardrails for risks such as PII exposure and prompt injection. The announcement also covers end-to-end tracing, coding-agent analysis with Genie, incident investigation with Lakewatch, ecosystem integrations, and Managed Omnigent on Databricks in Beta.


### [Introducing Genie One, Genie Agents, and Genie Ontology](https://yomu.fyi/post/introducing-genie-one-genie-agents-and-genie-ontology.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sydney Sundell, Ken Wong, Elise Georis
- Published: Jun 16, 2026
- Type: Announcement

Databricks announces Genie One, Genie Agents, and Genie Ontology to help enterprises answer business questions and act on data whose context is scattered across dashboards, queries, documents, tickets, and chats. Genie One connects data and business tools through Lakehouse federation, Lakeflow Connect, native integrations, Slack, Teams, mobile apps, schedules, alerts, document creation, custom skills, and MCP support. Genie Agents evolve Genie Spaces into domain-specific agents that can reason over structured and unstructured data, execute multi-step workflows, and be created from a prompt. Genie Ontology builds a permission-aware living graph from enterprise assets, weighting sources by authority, usage, certification, and freshness. In an internal 28-question benchmark, Genie answered 84.5% correctly on the first attempt and delivered twice the speed of the strongest coding agent.


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