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ForensicDbg launches private beta of Windows crash debugger with AI/MCP integration

A developer has released ForensicDbg, a post-mortem debugging tool for native Windows x64/x86 crashes, now in private beta. The tool exposes its debugging engine to AI systems via an MCP server, aiming to improve AI-assisted crash analysis while reducing token usage. As an example bug, the release notes describe a null-pointer dereference in CTextWindowBase::SetSelectionBegin that occurs when a disassembly panel is clicked before any code lines are loaded.

Foremerge open-sources Git-based tool to flag intent conflicts among AI coding agents

Foremerge is a new open-source coordination protocol that sits on top of Git to let multiple AI coding agents, such as Claude Code, Codex, or Cursor, share their intended changes before writing code. Each agent registers what function or component it plans to modify in a shared SQLite-backed list stored inside the project's .git folder, so overlapping or contradictory plans can be flagged before they merge. The current release, version 0.5.0, is a local-first, pre-1.0 MVP with a working CLI, JSON API, MCP server, and conflict detector, though it doesn't yet support coordination across multiple machines.

Apple adds Safari MCP server for AI coding agents in Safari 27.0

Apple's WebKit blog detailed Safari 27.0's developer features, headlined by a new Safari MCP server that lets AI coding agents like Claude Code or Codex control the browser to inspect DOM elements, network requests, screenshots, and console output. Developers enable it through Safari's Developer settings after turning on features for web developers, and Apple says the server runs locally without sending data externally.

Scry launches programmable web search API with MCP support and congestion pricing

Scry is a new search-over-the-web service built for AI agents, exposed through the Model Context Protocol as well as a direct HTTP API. Developers connect tools like Claude Code, Codex, or Cursor to a single MCP endpoint, authenticate via OAuth or an API key, and query a rolling crawl of high-information web pages using a SQL-like interface. The service also introduces congestion-based pricing tied to usage load.

Google opens Home MCP server, letting AI agents like Claude and ChatGPT control smart devices

Google has launched early access to a Model Context Protocol (MCP) server for Google Home, enabling AI agents such as Claude, ChatGPT, Hermes, OpenClaw and Google Antigravity to interact with connected smart home devices. Through natural language, users can review camera summaries, monitor activity, control devices, and build custom dashboards, after linking their agent via a Google Cloud project and granting permissions.

Chat-Man launches MCP server giving AI agents programmatic WhatsApp access

A developer named Fabian released Chat-Man, a tool that connects WhatsApp to AI agents through an MCP server, allowing programmatic reading, searching, and sending of messages. It also includes a web interface for non-technical users to manage groups, contacts, and bulk messaging, plus webhook support for starred conversations. Fabian built it after needing WhatsApp integration across multiple personal projects and wanting to avoid paying for separate integrations each time.

Paper2Agent tool converts research papers into interactive AI agents

Stanford researchers led by James Zou built a system called Paper2Agent that automatically transforms a scientific paper's text, code and data into an AI agent acting as a stand-in for its corresponding author. The tool deposits a paper's materials onto an MCP server, has AI agents build tools that apply the paper's methods to new data, and lets scientists query the resulting agent in plain language via any large language model. In one test, it built an agent for the AlphaGenome paper in about 45 minutes for $14, and that agent answered genetics questions with near-perfect accuracy.

Paper2Agent turns research papers into AI agents via Claude Code pipeline

Researchers built Paper2Agent, a system that converts a scientific paper and its accompanying codebase into a functional MCP server accessible through an AI agent interface. It uses a multi-agent architecture built on Claude Code's agent SDK, with a central orchestrator directing specialized sub-agents through a six-step process covering repository discovery, environment setup, tutorial scanning, and execution auditing.

Meta launches WhatsApp Business Tools MCP for AI agent-driven setup

Meta introduced a new MCP (Model Context Protocol) server called WhatsApp Business Tools MCP, letting AI coding agents like Claude, Cursor, Codex, or ChatGPT directly configure WhatsApp Business accounts. Instead of manually navigating the Developer Console, Business Manager, and API references, businesses can now simply describe what they need to an AI agent, which handles account creation, phone verification, Cloud API registration, and template management.

New CLI tool 'geiger' audits every AI agent and MCP server on a computer

A developer released geiger, a free command-line utility that scans a machine for installed AI agents, harnesses, MCP servers, plugins and extensions, then reports in plain language what each one can access. It runs via npx with no installation or account required, only reads configuration files and directories, and writes nothing to disk unless a JSON export is explicitly requested.

Applied Compute launches AC2, a cloud platform for training open-weight AI models

Applied Compute has introduced Agent Cloud (AC2), a platform designed to give researchers a unified environment for training, running inference on, and continually updating open-weight AI models. The system includes a CLI, SDK, and MCP server, letting developers set it up through coding agents like Claude Code, Cursor, or Codex, or manually via a quickstart guide.

Robotics engineer finds LLM coding assistants struggle with visual debugging tasks

A robotics developer describes attempting to offload the tedious process of visually debugging robot code to an LLM coding assistant equipped with an image encoder and MCP server access to a visualizer tool. The experiment largely failed: the assistant lacked an intuitive sense of what correct robot behavior looks like and struggled to navigate the debugging GUI, taking minutes to reach a viewpoint the developer could reach in seconds.