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161 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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Workflow1111 rebuilds most of AUTOMATIC1111’s stable-diffusion-webui feature set as a single Gradio Workflow canvas. It combines eleven media pipelines and 73 nodes for text-to-image, refinement, image-to-image, prompting, interrogation, detection masks, annotators, upscaling, background removal, PNG metadata, and image-to-video. The implementation uses four operator kinds—Python fn functions, model calls through InferenceClient, Space calls, and Hub dataset rows—with parallel branches for independent work; 32 of 36 operator nodes are fn nodes, and 22 run in-process without network calls. Models can run through Inference Providers, Spaces, or a local GPU, while output nodes automatically become REST endpoints and MCP tools. The result is a browser-accessible, OAuth-enabled workflow that can also be duplicated, rewired, deployed, and called from code.
Xcode 26.3 introduces a native integration with the Claude Agent SDK, bringing the same harness that powers Claude Code into Apple’s IDE for longer-running, autonomous coding work. Claude can receive a goal, inspect a project’s full file structure, determine which files to change, modify them, and iterate, while searching Apple’s documentation when it needs framework or API guidance. It can also capture Xcode Previews to visually verify SwiftUI interfaces, identify problems, and refine its implementation, and it supports subagents, background tasks, and plugins. The integration is additionally exposed through the Model Context Protocol, allowing Claude Code users to connect to Xcode and capture Previews from the CLI. Xcode 26.3 is available as a release candidate to Apple Developer Program members, with an App Store release coming soon.
Anthropic is acquiring Stainless, an SDK and MCP server tooling company founded in 2022, to expand how Claude and other agents connect to data and tools. Stainless has generated every official Anthropic SDK since the API’s early days and is used by hundreds of companies to produce SDKs, CLIs, and MCP servers from API specifications. Its tooling supports TypeScript, Python, Go, Java, and other languages, with libraries designed to feel native while remaining fast and reliable. The companies say combining their teams will advance developer experience and agent connectivity on the Claude Platform, while Stainless’s founder, Alex Rattray, said the team will continue its work within Anthropic.
Closed or cloud-based coding assistants can expose work to external services, so the article presents a local, open-source alternative built around OpenCode. OpenCode is an AI coding agent—not an LLM—that runs as a terminal interface, desktop application, or IDE extension and connects to models through the Model Context Protocol (MCP). The setup installs OpenCode, selects a local model runtime such as Ollama for AMD/NVIDIA GPUs or OpenVINO Model Server with INT4 Qwen3-8B for Intel integrated graphics, then configures opencode.jsonc and an editor plug-in. Examples cover Qwen3:14b with Ollama, Podman-based OpenVINO serving, and NetBeans integration; the resulting assistant can answer queries and edit files after permission is granted, although local inference may be slow and generated advice can be unreliable.
YouTrack announces live webinars for teams considering migration from Atlassian after Atlassian’s stated discontinuation of Data Center sales and support. The worldwide English-language session is scheduled for September 30, 2026, at 2:00 pm CET on YouTube, alongside a Japanese Zoom session and a Russian-language regional session hosted by Noventiq on October 28. The agenda covers migrating projects, users, and data from Jira, Confluence, and Jira Service Management, with a live demo, customer stories, pricing, licensing, deployment options, user packs, and partner support. It is aimed at administrators, project managers, and team leads, and presents YouTrack as a place to combine project management, documentation, and customer support. The Japan session additionally covers workflow automation and connecting AI agents through MCP, while registration for the Asia and Caucasus session is not yet open.
Salesforce Health Insights addresses enterprise org health monitoring by combining configuration metadata with runtime telemetry to identify and prioritize remediation. The team evolved a static report into an in-app experience with durable finding dispositions, approximately 400+ signals across domains, and standards informed by Well-Architected principles and implementation anti-patterns. A shared harmonization layer reconciles fragmented datasets, connector patterns link runtime activity to metadata components, and the system preserves data origin, freshness, and access requirements while keeping deterministic findings distinct from externally supplied context. Prioritization combines findings with request volume, application and database CPU, call behavior, and peak-business-hour traffic to rank next best actions by criticality and effort. A headless JSON schema supports interfaces including an MCP connection, while the deterministic core is intended to produce dependable recommendations and extend access beyond the primary interface.
MCP Toolbox for Databases addresses the gap between an AI agent’s text queries and ClickHouse’s SQL and vector interfaces by translating text into embeddings for both ingestion and search. A flat YAML configuration declares a Gemini embedding model, associates parameters with it through embeddedBy, inserts content and vectors, and ranks matches with ClickHouse’s cosineDistance function; the agent receives ranked rows without handling vectors. The post walks through installation, prebuilt ClickHouse tools, custom tools, toolsets, and an end-to-end synthetic-corpus run using Toolbox 1.9.0 and ClickHouse Cloud 26.4.1. At the demonstrated 57-row scale, search uses exact O(n) brute force because the HNSW index adds overhead, while larger datasets can use approximate search. Limitations include Gemini-only embeddings, a hardcoded SEMANTIC_SIMILARITY task type, opt-in REST endpoints, and restart-required configuration changes.
This tutorial builds an AIOps workflow for diagnosing and remediating a deliberately induced CPU incident in a product catalog application running on an AWS EC2 instance with RHEL. An Ansible investigation playbook collects CPU utilization, top processes, application status, and HTTP health evidence, which Claude Code analyzes through MCP to identify the Python process as the likely contributor and recommend the approved “AIOps - Remediate High CPU” Job Template. AI does not access the host directly: Ansible Automation Platform retains responsibility for credentials, inventory, execution, and remediation. The remediation changes the application from HTTP 503 and degraded to HTTP 200 and healthy, while a final health check confirms recovery and completes the detect-investigate-analyze-recommend-execute-verify sequence.
Kotlin Toolchain 0.12.0 extends its multiplatform build and publishing capabilities with multiplatform library publication, preview support for Wasm applications, command-line Compose Hot Reload, and broader IDE and terminal improvements. Libraries use the existing publishing configuration and produce a common API, per-platform artifacts, sources, module metadata, plus commonized and platform-specific cinterop bindings; Compose Multiplatform resources are not yet included. wasm-js/app modules can be built into ready-to-use web applications, run with kotlin run, customized through resources, and supplied transitive npm dependencies from Kotlin Multiplatform libraries. The release also reduces default Maven Central files, supports Compose Hot Reload through an MCP server, and recommends project-root-relative // dependencies while retaining the older syntax. It raises minimum compilation requirements to JDK 17 and Kotlin 2.2.20, updates bundled versions including Kotlin 2.4.10 and JDK 25, and recommends IntelliJ IDEA 2026.2.1 or newer for the latest features.
Sprites are presented as disposable cloud computers for running coding agents and as tools agents can create for testing, debugging, benchmarking, and multi-service work. The post describes MCP as the transport and authorization layer, while progressive disclosure comes from plugins and skills that reveal capabilities only when needed; CLI, REST, and machine-readable /.sprite/llm.txt remain available. The hosted MCP server returns file reads as MCP resources and attaches safety annotations, marking read-only and destructive operations and flagging exec and service_start as crossing the Sprite boundary. Agents connect through sprites.dev/mcp and authenticate to a specific Fly.io organization, with configurable scoping, a default five-Sprite cap, and an mcp- name prefix. The design emphasizes durable filesystems, real networks, and near-zero idle cost instead of stateless sandboxes, while the post argues these properties make disposable computers practical for agent workflows.
TeamCity 2026.2 makes pipelines generally available on TeamCity Cloud and On-Premises, moving them beyond the Early Access Program and adding branch-aware editing, failure-tolerant job execution, promotion, job debugging, and VCS-unbound workflows. The release also expands AI capabilities: AI Assistant supports Bring Your Own Key for Anthropic, OpenAI, Google Gemini, other supported models, or a local LLM, while MCP adds pipeline management tools and OAuth/PKCE authorization. Additional changes include configurable retries for failed dependencies, IDEA plugin improvements, GitHub pull-request matching by source branch, and a choice of server- or agent-side Kotlin DSL compilation. The pipeline MCP post and delete tools require manually enabled Brave mode, and DSL compilation on agents is described as less restricting and more secure but subject to trade-offs. Bug-fix updates 2025.11.8 and 2026.1.4 also ship.
CLion 2026.2.2 adds an AI skill for diagnosing ARM Cortex-M hard faults through dedicated MCP tooling. Embedded debugging typically requires decoding CFSR and HFSR, matching the faulting program counter to disassembly, and reproducing crashes because default handlers often report only that a fault occurred. When triggered by a stopped hard-fault-family handler or relevant fault evidence, the skill provides decoded status registers, the hardware-stacked exception frame, SVD-decoded peripheral registers, disassembly, and nearby memory to an agent. It supports any debugging tool, including Lauterbach TRACE32, Segger J-Link, and ST-LINK, and can produce a bug location and fix, with optional rerunning to verify the program. The skill is enabled by default but requires CLion’s MCP server; it works in AI chat and CLI mode for Claude Code and Codex, while GitHub Copilot supports CLI mode only.
The strongest submissions in the Google for Startups AI Agents Challenge repeatedly used four engineering patterns rather than merely labeling prompt chains as multi-agent systems. They exposed internally used tools through bidirectional MCP, used typed events and asyncio queues to let independent agents run concurrently, and routed fallback outputs through one shared validation function. They also reduced inference cost with tiered routing: regex or a cheap classifier handled simple intent before requests reached an expensive reasoning model, with one team reporting more than 40 percent handled before a real model call. The post argues these practices improve bounded data access, latency, resilience, and cost without requiring larger teams or newer models, and notes that Agent Development Kit and Agents CLI submissions showed them frequently.
The page presents six featured Google developer articles spanning multi-agent systems, content credentials, TPU optimization, agent plugins, Go, and Genkit Go. One article identifies bidirectional MCP, async event buses, unified validation, and tiered routing as recurring patterns in successful AI Agents Challenge submissions. Another introduces Credentio, an open-source C++ library for local-first C2PA validation, while a HeyGen case study reports a 1.86x speedup for Avatar IV on Trillium TPUs using torchax, XLA, FSDP, Ulysses sequence parallelism, and custom kernel/compiler optimizations. The collection also covers Agent Plugins 1.0.0’s plugin.json directory specification and Genkit Go’s progressive-disclosure Agent Skills based on SKILL.md bundles. A final article argues that Go’s compiler, toolchain, readability, and backward compatibility provide guardrails for reviewing and maintaining AI-generated software.
Databricks engineers investigated whether silent failures in MCP tool calls were driving hidden AI-agent costs as usage increased. Unity Gateway emitted OpenTelemetry traces for every invocation, recording tool names, arguments, errors, token counts, latency, and session IDs in a table that Genie One could query with plain-English questions. In a 24-hour sample, the analysis identified seven Jira and Google Drive/Docs bugs responsible for an estimated $499,000 annually in wasted tokens and 12,023 hours of agent wait time, or $1.2 million in lost productivity. The most costly patterns included under-specified interfaces, incompatible argument shapes, and cryptic or misleading errors that triggered retries and recovery loops. Engineers fixed the issues with a coding agent, completing the find-quantify-fix cycle in about an hour; the post recommends designing tools to tolerate reasonable LLM inputs and continuously tracing failures.
MCP 2026-07-28 removes the initialize handshake and Mcp-Session-Id, making the protocol core stateless and changing how remote servers scale on AWS. Requests carry protocol version and client context, while server/discover can expose versions, capabilities, and identity; continuity remains possible through server-minted identifiers passed in tool arguments and stored application state. It covers round-robin routing, header-based gateway signals, cache freshness fields, W3C Trace Context, idempotent tools, and Multi Round-Trip Requests for input-dependent calls. It recommends retaining sticky routing and session stores for 2025-era clients, then migrating after legacy traffic reaches zero and validating against the official conformance suite. The conclusion is that the revision aligns remote MCP deployments with AWS Well-Architected practices and permits removal of infrastructure required only by the old protocol.
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.
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