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165 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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Auth0 is launching Early Access for Client App, or Requesting App, capabilities that support Cross App Access (XAA) for AI assistants, developer tools, and autonomous agents. In the Enterprise-Managed Authorization (EMA) model, an employee’s corporate SSO login can authorize access to external APIs or Model Context Protocol (MCP) servers through the IETF Identity Assertion Authorization Grant (ID-JAG) draft specification. Auth0’s Token Vault brokers the multi-hop exchange: it stores an upstream enterprise refresh token, retrieves a short-lived ID-JAG, exchanges it with the downstream Resource App, and returns a standard access token to the backend. Setup uses dashboard toggles and connection settings, followed by a standard /oauth/token federated exchange, while the API requires no XAA-specific changes. The release also describes audit logging, enterprise IdP policy control, OIN distribution, and caching downstream tokens because ID-JAG exchanges are rate-limited to 50% of the tier’s standard Auth API limit.
Agentic commerce is moving from AI-assisted product discovery toward purchases completed by agents, but retail systems often cannot reliably establish the shopper’s identity or spending authority. The post describes four phases—product discovery, checkout functions, delegate-to-buy, and agent-to-agent commerce—and notes standards including UCP, ACP, and AP2. Auth0’s approach combines user authentication, asynchronous authorization, and authentication for MCP, using CIMD-based agent registration, OAuth, and on-behalf-of token exchange to issue temporary, scoped transaction access. It presents third-party shopping agents and retailer-owned agents as two deployment models, with actions tied to an authenticated shopper and recorded for auditability. The proposed identity layer can sit alongside an existing identity provider while supporting emerging protocols and future autonomous purchasing.
Managing modern large-scale Kubernetes fleets across distributed clusters can make troubleshooting slow because administrators must build manual search queries and sift scattered outputs. The post presents a Technology Preview MCP server for Red Hat Advanced Cluster Management that translates natural-language prompts into structured searches across managed clusters, deployed with Helm in the hub cluster. OpenShift Lightspeed is configured through its OLSConfig custom resource to call the MCP endpoint, use a Kubernetes service-account token for internal authorization, and optionally skip approval for read-only searches. The examples cover finding clusters below OpenShift 4.21, locating pods in a specified cluster and namespace, and identifying an unhealthy VM across the fleet; one result finds dev-vm in an ErrorUnschedulable condition and provides diagnostic guidance.
Anthropic and HHMI Janelia Research Campus are opening a research preview of the Model Hardware Standard (MHS), a shared specification for AI agents to operate programmable laboratory and manufacturing devices safely. MHS replaces bespoke integrations with standardized drivers built around read and write primitives, device discovery, natural-language tags, generated reference files, and control through MCP, a command-line interface, or code files. The model-agnostic standard lets agents coordinate instruments, monitor operating data, adjust parameters in real time, and turn exploratory work into deterministic scripts; early projects covered protein assays, qPCR, microscopy, robotics, dose-response experiments, and quantum-laser stabilization. Reported results include serial dilution experiments running about three times faster and a QuEra laser-lock controller recovering 99.3% of the time, while the preview will inform safety evaluations and guidance before an open-source release.
Claude for Education is adding integrations with Canvas, Panopto, and Wiley to bring course materials, lecture transcripts, and peer-reviewed content into student conversations. Canvas LTI lets students use Claude inside courses, while Wiley and Panopto connect through pre-built MCP servers. Conversations are private by default, excluded from AI training by default, institutional data requests require formal approval, and self-serve data exports are limited by default. The announcement also covers new institutional partnerships, a tenfold expansion of the student ambassador program, and Claude Builder Clubs featuring hackathons, workshops, and demo nights. It frames responsible collaboration, ethics, privacy, and equitable access as necessary to avoid deepening existing divides while AI transforms education.
Claude Science is a beta AI workbench for scientists that brings databases, research tools, computing resources, and domain-specific workflows into one environment. Researchers can analyze literature, run multi-step studies, create figures and manuscripts, and revise them through conversation. Every generated artifact includes its code, environment, plain-language creation description, and message history, while a reviewer agent checks citations, calculations, and consistency between figures and code. A coordinating agent uses more than 60 curated skills and connectors across fields including genomics, single-cell, proteomics, structural biology, and cheminformatics; it can manage jobs on local systems, HPC clusters over SSH, or Modal, scaling from one GPU to hundreds. Beta access is available on macOS and Linux for Pro, Max, Team, and Enterprise users, with reported examples including faster glioma analyses and large literature reviews.
This webinar addresses the gap between impressive voice AI demos and reliable production deployments, focusing on global rollout barriers such as spam-flagged outbound calls, legal delays, latency, and custom integration work. It presents Twilio Voice and AI through the Guinness Price Index case, alongside reported examples from Docplanner and audibene, whose use of the Twilio platform is said to halve costs, double booking efficiency, and improve inbound reachability by 30 percent. A developer demonstration shows how Twilio Skills and an AI coding agent configure an initial setup, while an open-standard Model Context Protocol (MCP) Server and Twilio Agent Connect SDK replace growing custom glue code. It also covers Conversation Relay for real-time audio, Conversation Memory and Conversation Orchestrator for AI-to-human handoffs, and connections to privately hosted models to retain cloud and roadmap control. The session frames compliance, privacy, personalisation, and vendor lock-in as operational concerns in scaling voice AI.
Lore is an open-source, MIT-licensed tool from Notion for giving AI agents shared, persistent memory backed by Notion, addressing experiential knowledge, follow-up tasks, decision records, and procedures that can disappear between sessions. A Lore vault uses five related Notion databases—Projects, Topics, Memories, Entities, and Facts—with MCP tools for agents, Notion or a CLI for humans, and hooks intended to retrieve context and save information in the background. The design emphasizes memory maintenance: facts expire by default, relationships such as supersedes and conflicts_with clarify updates, and the CLI can identify orphaned or duplicated information. In a 500-query SkillRet retrieval evaluation, Lore achieved 84% success, while a paired model evaluation found seeded memory recovered about 46% of failures on tasks never solved without memory. The results indicate that relevant, well-maintained memory can improve agent performance, while stale or irrelevant content can harm it.
Scottish Water’s Capital Investment teams had project data, reports, and metrics available, but non-technical users often struggled to find answers, creating duplicated reporting and delays. SPARK addresses this access problem through a natural-language interface in Microsoft Teams: Copilot routes questions via a supervisor agent and MCP to the Databricks Genie Agent, which queries governed Unity Catalog data. The implementation curates use-case data in a gold layer, adds metric views as a semantic layer, and configures business rules, examples, benchmarking, feedback, and monitoring for accuracy and performance. Environment-parameterised Databricks Asset Bundles, separate environments, Azure DevOps approvals, Entra ID, and Key Vault support repeatable deployment. The post reports lookup journeys shrinking from around eight clicks or four to five search steps to a direct question, with an estimated 520 to 1,300 hours saved annually under a stated usage scenario.
MongoDB has launched the Atlas Managed MCP Server, a fully hosted service that lets AI coding tools connect to Atlas data without manual infrastructure setup. The server uses OAuth 2.1 to replace shared service accounts with individual user delegation, so agents act with the developer’s own permissions and every action is auditable. Developers can connect via one-click OAuth consent in tools like Claude Code, Codex, Cursor, Grok Build, and Devin AI, with no connection strings or credentials to manage. Admins can enforce read-only mode, configure token lifetimes, and revoke access from the Atlas UI or Admin API. The announcement also includes Atlas App Connections, native connectors for frontier AI tools, and an integration with Vercel v0, aiming to close the gap between prototype and production.
Notion’s engineering collection covers tools, product design, infrastructure, security, and data systems across a set of technical posts. It opens with an inside look at the hosted Notion MCP server, created in response to requests for AI agents to work directly with Notion workspaces; the path described runs from an open-source experiment to a hosted solution with custom tools for AI workflows. Other entries describe Lore’s conversion of conversations, decisions, tasks, and durable facts into Notion pages, a Spark-native lexical-search reindexer, and multi-region systems for search, analytics, AI, and event logging. The collection also reports CI guardrails for schema changes, security testing for Custom Agents, vector-search scaling by 10x with costs reduced by 90%, Spot Balancer for Spark on Kubernetes, and the architecture and data model behind Offline Mode.
Tool calling lets an AI model interact with external tools, APIs, and systems to retrieve information or perform actions beyond its training data, turning a conversational model into an agent that can act. The process is an application-mediated loop: the model recognizes when outside help is needed, selects a tool from schema-described options, emits a structured request—typically JSON—and receives the result as context for its response or next step. Available tools can support information retrieval, code execution, process automation, and smart-device or IoT monitoring, while complex tasks may chain multiple calls into a workflow. The piece also distinguishes tool calling from function calling, describing the latter as a subset, and presents MCP as a standardization layer for describing, discovering, and invoking tools, with Databricks Agent Bricks offering governed integrations and agent-development support.
Agent Plugins 1.0.0 is an open, vendor-neutral specification for packaging Agent Skills and MCP servers into portable plugins, addressing incompatible wrappers that force authors to maintain client-specific copies. It defines a plugin as a directory with plugin.json, skills/ subdirectories, mcp.json, and client-owned reverse-domain extension namespaces. The manifest fixes component locations, requires explicit MCP server types for stdio, Streamable HTTP, or legacy HTTP+SSE, and lets skills continue loading when an MCP entry fails. Version 1 deliberately excludes installation, distribution, permissions, sandboxing, trust verification, and user experience, leaving those concerns to clients and related ecosystem layers such as discovery and catalogs. Google is joining the Core Maintainers and says Agents CLI and Data Agent Kit support the format, while more product integrations are expected.
The 2026-07-28 Model Context Protocol specification release candidate addresses production bottlenecks caused by the earlier session-oriented HTTP model, including session pinning, sticky routing, lost state, and Redis-backed infrastructure. It removes the initialize/initialized handshake and Mcp-Session-Id, carrying protocol version, client information, and capabilities in _meta on each request while exposing method and name through HTTP headers. The update also introduces ttlMs/cacheScope caching, Multi Round-Trip Requests for elicitations and task progress, issuer and resource-indicator verification, and full JSON Schema 2020-12 support. The post says the stateless core enables ordinary load balancing, serverless deployment, and transparent failover, while SDK beta releases for TypeScript, Python, Go, and C# support migration.
Unity AI Gateway is generally available as a centralized way for enterprises to manage AI costs, security, governance, and model choice across agents and other AI assets. It positions Unity AI Gateway alongside Unity Catalog: Unity Catalog supplies identity, permissions, lineage, and auditing, while the gateway applies runtime guardrails and contextual policies across AI interactions. The gateway centralizes usage data in Unity Catalog, offering dashboards, Genie-powered analysis, granular cost attribution, budgets, hard spend caps, and a single-query API for frontier models from providers including Anthropic, OpenAI, Gemini, Kimi, and GLM. Smart Routing is in beta and dynamically selects models using quality, cost, performance, availability, and budget; the announcement says customers already use the gateway at scale, including more than one quadrillion tokens passing through it in the past year.
Media buying remains slowed by fragmented coordination across emails, spreadsheets, PDFs, and phone calls, leaving teams to research inventory, compare pricing, negotiate, and issue orders manually. The post presents an agentic workflow in which buyer and seller agents discover inventory, negotiate prices, and book deals through IAB Tech Lab’s AAMP standards, including AdCOM, OpenDirect, OpenRTB deals, and registry-based discovery and trust. Its Databricks implementation runs self-contained applications on Databricks Apps, with CrewAI agent crews using Databricks Foundation Model APIs and Claude models, Lakebase providing transactional Postgres state, and MLflow tracing capturing decisions and tool calls. The example processes a $200,000 Q3 Brand Launch across CTV and Linear TV with a $38 CPM ceiling, and is packaged as a Databricks Automation Bundle accelerator deployable with a single command.
Setting up a cloud-agent environment for the Cursor monorepo required treating development infrastructure as a product for agent users. The team aligned cloud VMs with local development by adapting utilities and setup scripts for Ubuntu, adding dependencies to a Cursor-defined Dockerfile, and introducing security controls for secrets and network access. To make the repository easier to operate, it built anydev to start services, route utility scripts, explain subcommands, and supervise long-running builds. Cursor Cloud MCP and Cloud Doctor then inspect environments, distinguish transient from salient failures, perform root-cause analysis, open high-confidence fixes, and improve workflows using traces. The resulting end-to-end testing and recorded demos helped cloud agents grow from about one in ten merged PRs in December to more than half, with the post identifying tool parity, documented skills, and core-workflow verification as readiness checks.
Grab’s AI support bot grew from an internal GPT-powered Level-0 service into the foundation for LLM-Kit, an internal framework now used by more than 500 services, alongside over 50 registered remote MCP servers and a company-wide LLM gateway handling billions of tokens monthly. The bot used a single-agent reasoning loop and a tool plane spanning Glean, Kibana, GitLab, Slack, and HTTP plugins, but scaling exposed missing evaluations, fragmented observability, provider-switching friction, and costly production plumbing. LLM-Kit addresses those problems with generated FastAPI services, LangGraph agent templates, OpenTelemetry tracing, configuration and secret-management integrations, evaluation routes, remote MCP tool discovery, and gRPC contracts, clients, health checks, and channel selection. The post concludes that framework concerns became platform concerns at 500 agents, leading to a gateway, remote MCP framework, and evals platform described as the series continues.
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.
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.
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