---
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

### [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
- Type: Explainer

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
- Type: Announcement

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
- Type: Announcement

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
- Type: Explainer

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
- Type: Explainer

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
- Type: Announcement

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.


### [Launch Week 6: Wrap Up](https://yomu.fyi/post/launch-week-6-wrap-up-up.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Apr 20, 2026
- Type: Announcement

Launch Week 6 concluded with five feature releases and one announcement covering email automation, content creation, developer tooling, tracking, and community events. Automations orchestrates lifecycle emails from application events with delays, waits, conditions, test runs before publishing, and real-time visibility into runs. The AI-extended editor turns URLs into on-brand drafts, supports inline block editing and mistake detection, and connects to agentic stacks through the Resend MCP server, while CLI 2.0 adds React-file sending, automation and event management, webhook listening, and AI-agent skills across more than 50 API commands. Custom tracking domains support click and open tracking, React Email 6 adds an embeddable editor and extensions API, and Resend Forward was announced as a San Francisco conference scheduled for October 21.


### [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
- Type: Announcement

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
- Type: Tutorial

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
- Type: Announcement

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.


### [AI Email Editor](https://yomu.fyi/post/ai-email-editor.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Apr 14, 2026
- Type: Announcement

Resend announces an AI-extended email editor designed to help users create emails that retain their brand voice while catching mistakes and reducing workflow friction. Users can provide a URL, and the AI extracts the brand, voice, and tone to generate a ready-to-send draft from existing content such as a blog post, changelog, or product page. During composition, selected blocks can be rewritten, rephrased, or improved, while pre-send checks cover deliverability, content compatibility, subject and preview, broken links, and typos. The editor also supports agents through the Resend MCP server, with access to the visual editor, real-time collaboration, cursor support, and presence indicators; the announcement says all features are live in the Broadcast and Template editors.


### [Build Better AI Agents: 5 Developer Tips from the Agent Bake-Off](https://yomu.fyi/post/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Frank Guan, Abraham Gomez
- Published: Apr 14, 2026
- Type: Tutorial

The Google Cloud AI Agent Bake-Off is presented as a test of building autonomous agents under severe time constraints for e-commerce, banking modernization, and startup go-to-market problems. Its five patterns recommend decomposing monolithic prompts into specialized sub-agents under a supervisor, designing modularly because model capabilities can quickly replace custom harnesses, and treating multimodality as native. It also advocates open protocols—including MCP, A2A, UCP, AP2, A2UI, and AG-UI—for agent and tool connections instead of bespoke wrappers. Finally, it separates probabilistic reasoning from execution: strict JSON schemas such as Pydantic validate LLM outputs before deterministic Python functions or SQL perform calculations and writes. The stated conclusion is that reliable production agents depend on rigorous software architecture rather than elaborate prompts or flashy single-shot demos.


### [Money Forward brings Cursor’s coding agents to product, design, and QA](https://yomu.fyi/post/money-forward-brings-cursor-s-coding-agents-to-product-design-and-qa.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 18, 2026
- Type: Explainer

Money Forward expanded Cursor coding agents beyond engineering to product, design, and quality assurance after developers began saving an estimated 15–20 hours per week. Adoption among engineers rose 30% within a week, and the company now reports more than 1,000 daily users; engineering examples include Rails optimization, Terraform deployments, and Vue-to-React migrations. For the company-wide rollout, its MEPAR department chose Cursor for minimal setup, visual browser capabilities, a unified workspace, model-agnostic asynchronous cloud agents, and context retrieval across complex codebases. QA uses MCP-connected Jira and Notion materials to generate test cases and Playwright scripts, reducing test-generation time by 70%, while product managers inspect repositories for requirements and designers prototype against live frontends and product analytics.


### [Securing our codebase with autonomous agents](https://yomu.fyi/post/securing-our-codebase-with-autonomous-agents.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Travis McPeak
- Published: Mar 16, 2026
- Type: Announcement

After a fivefold increase in pull-request velocity over nine months, the team found static analysis and rigid code ownership insufficient for security at scale. It built a fleet of Cursor Automations powered by cloud agents, webhook and GitHub integrations, and a security MCP deployed as a just-in-time serverless Lambda function. The MCP provides persistent data, semantic deduplication using Gemini Flash 2.5, and consistently formatted reporting and follow-up actions, while Terraform standardizes review and deployment. Four templates cover PR review, segmented vulnerability hunting, dependency patching with reachability analysis and tests, and daily invariant-drift monitoring. Agentic Security Review has run on thousands of PRs in two months and prevented hundreds of issues from reaching production; the team says more automations are planned.


### [Over 30 new plugins join the Cursor Marketplace](https://yomu.fyi/post/over-30-new-plugins-join-the-cursor-marketplace.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Kevin Niparko
- Published: Mar 11, 2026
- Type: Announcement

Cursor is adding more than 30 plugins to its Marketplace, including integrations from Atlassian, Datadog, GitLab, Glean, Hugging Face, monday.com, and PlanetScale. The plugins extend Cursor across infrastructure and productivity workflows: users can query observability data, manage GitLab repositories and pipelines, work with PlanetScale schemas and queries, handle Atlassian issues and backlogs, search company knowledge in Glean, and manage monday.com boards and tasks. Each plugin bundles capabilities such as MCPs with skills that instruct an agent how to use them, and those tools can support manually started or automatically invoked cloud agents. Cursor also supports community-built plugins, while Teams and Enterprise admins can create private team marketplaces. Automations can run agents on schedules or events with custom instructions.


### [Unfortunately, Sprites Now Speak MCP](https://yomu.fyi/post/unfortunately-sprites-now-speak-mcp.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Mar 10, 2026
- Type: Announcement

Sprites are disposable cloud computers with durable filesystems, near-zero idle cost, and support for running coding agents and agent-managed workloads. The post announces an MCP interface, allowing Claude Desktop and other MCP-compatible agent tools to authenticate to a Fly.io organization and create or manage Sprites. It argues that MCP is aesthetically and operationally inferior to command-line tools and discoverable APIs because loading tool descriptions consumes context and can overemphasize capabilities an agent does not need. For agents that can run shell commands, the recommended pattern is a short skill description backed by progressive CLI or API discovery; MCP sessions remain a fallback for agents without command execution. Session guardrails can restrict the organization, cap Sprite creation, and apply name prefixes, while the broader design favors real filesystems and networks over stateless sandboxes.


### [Build agents that run automatically](https://yomu.fyi/post/build-agents-that-run-automatically.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jack Pertschuk, Jon Kaplan & Josh Ma
- Published: Mar 5, 2026
- Type: Announcement

Cursor Automations introduces always-on agents that run on schedules or react to events from Slack, Linear, GitHub, PagerDuty, or custom webhooks. Each invocation spins up a cloud sandbox, uses configured models and MCPs, verifies its output, and can draw on memory from past runs. The product supports review and monitoring workflows, including security audits on pushes to main, risk-based codeowner assignment, and PagerDuty-driven incident investigation that proposes fixes. Other automations generate weekly repository digests, add tests after merged code, triage Slack bug reports through Linear, and assemble cross-tool dashboards. Examples from Cursor and Rippling report faster incident response and reduced repetitive work, while Runlayer describes a software factory built with Runlayer MCP and plugins.


### [How technical support at Cursor uses Cursor](https://yomu.fyi/post/how-technical-support-at-cursor-uses-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Kody Fisher & Zach Hudson
- Published: Mar 3, 2026
- Type: Explainer

Cursor’s technical support team treats customer investigations as research problems, using a single session to combine code, logs, team knowledge, and past conversations. Engineers typically start in Ask Mode, use semantic search across a locally indexed codebase and documentation, and group related repositories with multi-root workspaces. MCP servers bring customer data, Datadog logs and traces, Slack and support history, runbooks, account details, and ticketing context into those investigations; Linear MCP turns evidence into escalations, while a Slack-mentioned cloud agent can open documentation pull requests. Slash commands, Rules, Skills, and parallel subagents automate repeated work, including failure analysis, known-issue searches, ticket writing, and customer-response drafting. The team reports that more than 75% of support interactions run through Cursor, with stated throughput gains of 5–10x and estimated productivity improvements of up to an order of magnitude.


### [How we built security into Custom Agents](https://yomu.fyi/post/how-we-built-security-into-custom-agents.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Sean Keenan, Jacob Brackett
- Published: Feb 27, 2026
- Type: Explainer

Custom Agents were designed for collaborative workspaces where useful access must be balanced against dangerous actions. They use a build-from-nothing model with no default read, write, or interaction permissions, then add resource-level controls such as page-level access, Slack “read and reply,” email confirmation, and selected MCP tools. Runtime safeguards combine permission controls with prompt-injection mitigations, monitoring of external data, warnings, owner confirmation for risky actions, and remediation such as deleting an incorrect Slack message. Alpha testing across Notion and customers produced more than 3,000 internal and 25,000 customer agents, and exposed overly broad Slack write access that could reach #general. That finding led to the custom permission and reinforced the case for first-party integrations, while planned work includes admin-approved broader scopes, Workers integrations, and additional permission modes.


### [Introducing Custom Agents](https://yomu.fyi/post/introducing-custom-agents.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Akshay Kothari
- Published: Feb 24, 2026
- Type: Announcement

Notion has made Custom Agents available to Business and Enterprise plans as autonomous AI teammates for recurring work across Notion, Slack, Mail, Calendar, Figma, Linear, and custom MCP servers. Users describe a job and set triggers, while agents can answer questions, route tasks, compile updates, and run workflows across connected tools; setup includes an agent-written instruction set and tool configuration. The announcement describes enterprise controls including permissions, version control, usage analytics, logged runs, reversible changes, alerts, auto-pause, and administrative connection controls, while noting prompt-injection risks and planned guardrails. Custom Agents are free through 3 May 2026, then consume Notion credits based on work, with the seat price and other listed AI features unchanged; early testers have built over 21,000 agents.


[Newer posts](https://yomu.fyi/topic/mcp/page/6.md) · [Older posts](https://yomu.fyi/topic/mcp/page/8.md)