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Cloudflare is rebuilding Wrangler into a unified CLI that exposes all ~3,000 API operations across 100+ products through a generated, consistent interface designed primarily for AI agents. Local Explorer adds a local dashboard to introspect simulated resources (KV, R2, D1, Durable Objects) during development, using the same API structure as production. Together, these tools address the shift toward agents as primary API consumers by eliminating inconsistencies that cause agents to fail and by giving developers visibility into what their agents are doing locally. Discover the benefits of the CF CLI Local Explorer tool in our recent blog article. Take your Cloudflare experience to the next level with this handy command-line interface feature.

Update: Pulsedive now has a dedicated documentation site with a complete API reference, live playgrounds, and MCP server support for AI-assisted development. Check it out: https://lnkd.in/eGX8JUm4 Announcement: https://lnkd.in/egDhks4t

Model Context Protocol went from "interesting spec" to "must-have infrastructure" in about 4 months. Here's what the ecosystem looks like now and which servers are actually production-ready. The winners (stable, daily-driver quality): - Filesystem MCP: Read/write/search across any directory. Every agent needs this. - Git MCP: Full git operations without shell escaping headaches. - Playwright/Patchright MCP: Browser automation that actually handles anti-bot detection. - Database MCPs (Postgres, SQLite): Direct query access without building API wrappers. Growing fast: - Google Workspace MCP: Gmail, Drive, Calendar, Docs, Sheets in one server. Replaced 5 separate integrations for us. - PDF/Document MCPs: Extract structured data from uploaded files. Game-changer for intake workflows. - Video analysis MCP: Feed video files, get structured descriptions. Vertex AI under the hood. Overhyped (for now): - "Everything" MCPs that try to wrap 50 APIs in one server. Jack of all trades, stable at none. - Memory/knowledge-graph MCPs. Interesting concept, but markdown files in known paths still win for agent state management. The pattern that works: Small, focused MCP servers. One server per domain (email, browser, files, database). Each does one thing reliably. Chain them with your agent's native reasoning, not with another orchestration layer on top. Total MCP servers on the official registry: 2,400+. Servers I'd actually trust in production: maybe 30. Which MCP servers are in your daily stack? #MCP #AITools #DevTools #LLMOps #ClaudeCode

Had this same thought for some time. Not only code, comercial llms also benefit from interacting with highly qualified and experienced engineers sharing their thoughts and correcting llms. In effect llms are now being trained by distilled knowledge of product engineers from all product companies. The winners at the end of speed run phase of comercial llms will be the those companies that created and operate the models.

For developers building the "Agentic Web," observability is the biggest hurdle. When an AI agent executes code in a remote sandbox, you need the same level of control you have on your local machine. Today we’re launching the Cloudflare CLI and Local Explorer. These tools provide a bridge between your local development environment and our global edge, allowing for real-time debugging, terminal access, and filesystem inspection of AI agent workloads. #AgentsWeek https://cfl.re/4tHEyB5

This is huge for Cloudflare! Our team at Vigilbase is already exploring how we can leverage the Cloudflare CLI via our Aegis agentic platform, to help customers manage their Cloudflare resources at light speed. Keep an eye out for news soon! 👀

Your Postman collections just got a lot more useful. Postman's MCP server is now listed in the Google Antigravity IDE MCP store. Install it, and Antigravity's AI agents get direct access to your collections, environments, and workspaces. No config required. What does that actually unlock? Google Antigravity can run your Postman collection as a test suite and get structured results back. It can read the real request definition from your collection when writing integration code, instead of guessing at the contract. It can scaffold a full collection from an OpenAPI spec and publish it to your workspace. If you're already on Postman, there's nothing to rebuild. Just install from the store and go. Read the full walkthrough: https://lnkd.in/gc7uFYCz

How do you take a single-tenant open-source MCP service and make it work for a multi-tenant managed platform, without forking the code? Wrap, don't fork. In his latest deep dive, Software Engineer Amin Ghadersohi walks through how Preset extended the Apache Superset™ open-source MCP service with the enterprise layers needed to bring AI-native analytics to production: → Multi-tenant workspace isolation: every MCP request is routed to the correct workspace with complete data isolation → Enterprise authentication: JWT + OAuth 2.0 (PKCE) via Auth0, supporting both programmatic and interactive workflows like Claude Desktop → Production deployment: dedicated K8s pods with HPA, session affinity, Datadog observability, and per-workspace feature gating The best part? Zero divergence from open source. Every improvement upstream flows directly to Preset customers. Read more: https://hubs.li/Q04ccgQG0 #mcp #apachesuperset #ai #dataanalytics #opensource #enterpriseai

In a second video of the series "Understanding ScaleOut Active Caching," discover how ScaleOut Active Caching™’s API modules enable developers to deploy application code to ScaleOut’s distributed cache using custom data structures and client APIs. Learn how these modules accelerate application performance, reduce network overhead, increase scalability, and simplify design. Watch the full video here: https://lnkd.in/gy8TNHCg   The video first explains the limitations of traditional caching techniques, including storing objects as uninterpreted blobs and using predefined data structures like hash sets and lists. Accessing blobs can create high network traffic that impacts performance. Predefined data structures reduce network usage, but they only cover specific use cases.   Next, see how ScaleOut Active Caching API modules provide a faster, more flexible and reliable alternative. Developers can use API modules to interpret cached objects as strongly typed data structures using client APIs written in C# or Java. They can deploy application code to the distributed cache to implement custom cache accesses and analytics.   A real-world e-commerce example demonstrates how application-specific APIs manage shopping carts and retrieve only the data needed, making cache operations more efficient. For example, they can access cart items by category or implement cart analytics and return results.   You’ll also learn how API modules enable faster, more maintainable applications and scale performance. In addition, you will see ScaleOut Active Caching’s intuitive UI for deploying API modules and performing analytics, such as aggregating, querying, and visualizing live data with assistance from generative AI. #DistributedCaching #GenerativeAI #InMemoryComputing

Worth reading for anyone building real AI systems with MCP. The article is practical, detailed, and focused on what it takes to connect agents to production systems rather than keeping the discussion abstract. This matches a lot of what I see while working on NLQ, MCP servers, and agent services. In practice, the design choice is usually driven by end requirements — especially latency, cost, reliability, and action boundaries. Very often, the better path is a minimal MCP server plus clearly defined skills, not a wide-open tool surface. And when that is combined with AG-UI and a strong MCP app/host approach, it creates the foundation for a much better user experience. https://lnkd.in/dKsc77Wi