# MSP MCP MSP MCP is a family of Model Context Protocol servers that expose Managed Service Provider (MSP) tooling to AI assistants. Each server wraps a vendor's API -- PSAs (Autotask, ConnectWise, HaloPSA), RMMs (Datto, NinjaOne, Atera, Syncro), documentation platforms (IT Glue, Hudu, Liongard), security tools (SentinelOne, Huntress, Blumira, RocketCyber), email security (Proofpoint, Avanan, Abnormal, Mimecast, IRONSCALES, KnowBe4, SpamTitan), incident management (Rootly, PagerDuty), and more -- as MCP-compliant tools so an MSP technician can ask Claude to triage a ticket, check device health, or pull documentation in plain English. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 4 engineering patterns behind the strongest AI Agents Challenge submissions DevFeed: [4 engineering patterns behind the strongest AI Agents Challenge submissions]() Original publisher: [Read original article]() Author: Sergio Villani Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [AI Bots](), [MSP MCP]() Tags: [agents](), [ai-agents](), [cost](), [engineering](), [event](), [inference](), [latency](), [mcp](), [mcp-server](), [routing](), [validation]() ### AI overview An article on engineering patterns found in leading AI Agents Challenge submissions. It highlights bidirectional MCP, asynchronous event buses, unified validation for model fallbacks, and tiered routing to make agentic workflows more resilient, faster, and less costly. ### Source excerpt The recent Google for Startups AI Agents Challenge revealed that the most successful multi-agent systems rely on foundational software engineering patterns rather than just raw model power. Winning architectures consistently implemented bidirectional MCP for seamless inter-agent communication, async event buses for parallel execution, strict unified validation for model fallbacks, and tiered routing to minimize expensive inference calls. By prioritizing these structural practices over simple linear prompt chains, developers can build more resilient, low-latency, and cost-effective agentic workflows. ## How MCP Toolbox turns agent text into ClickHouse vectors DevFeed: [How MCP Toolbox turns agent text into ClickHouse vectors]() Original publisher: [Read original article]() Author: Pete Hampton Published: 2026-09-07T09:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [MSP MCP]() Tags: [ai-agent](), [clickhouse](), [databases](), [embedding](), [gemini](), [go](), [google](), [how-to](), [mcp](), [search](), [sql](), [vector]() ### AI overview A tutorial for configuring Google's MCP Toolbox with ClickHouse to embed text during inserts and searches, then return cosine-ranked results without a separate embedding service. ### Source excerpt Google's MCP Toolbox for Databases embeds agent text into vectors on insert and search, then lets ClickHouse rank the results - no embedding service to build or maintain. Here's how to set it up, and what it looks like end to end. ## Give your agent Neon tools DevFeed: [Give your agent Neon tools]() Original publisher: [Read original article]() Author: Andre Landgraf Published: 2026-09-03T12:00:00Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [MSP MCP]() Tags: [agent](), [api](), [mcp](), [mcp-server](), [openapi](), [product](), [sdks](), [tools](), [workflows]() ### AI overview Neon announces @neon/tools, which adapts its SDK client into typed tools for agents and expands the hosted Neon MCP Server. The article explains progressive tool discovery and why agent-oriented tools should bundle workflows rather than mirror raw REST operations. ### Source excerpt Today we're launching @neon/tools, a package that turns the @neon/sdk ergonomic client into typed agent tools with adapters for MCP, Mastra, and Eve. We're also using it to expand the hosted Neon MCP Server. ## Your Agent Speaks MCP. Give It a Computer. DevFeed: [Your Agent Speaks MCP. Give It a Computer.]() Original publisher: [Read original article]() Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [The Fly Blog]() Topics: [MSP MCP](), [AI Bots]() Tags: [agents](), [api](), [auth](), [cdn](), [claude](), [claude-code](), [cli](), [close-to-users](), [cloud](), [codex](), [cursor](), [deploy-app-servers](), [docker](), [elixir](), [filesystems](), [fly](), [fly-io](), [gemini](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [mcp](), [mcp-server](), [networking](), [postgresql-clusters](), [servers](), [skills]() ### AI overview The article presents Sprites as disposable cloud computers for coding agents and explains using their API through MCP. It argues that MCP transport and progressive capability disclosure can work together, including through plugins and skills. ### Source excerpt Sprites are disposable cloud computers. They appear instantly, always include durable filesystems, and cost practically nothing when idle. They're the best and safest place on the Internet to run agents and we want you to create dozens of them. Sprites are a place to run agents; the first thing you should think to do with a new Sprite is to type claude (or gemini or codex). We've put a lot of effort into making sure coding agents feel safe and happy when they're on Sprites, because, to (probably) quote John von Neumann, "happy agents are productive agents." What's less obvious about Sprites is that they're great tools for agents. Want three different versions of a new feature? A test environment? An ensemble of cooperating services? It's super handy to be able to start your prompts, "On a new Sprite, do...". The Sprites API is simple, discoverable, and designed for this use case. The only real question is how your agent reaches it. For most of you the answer is MCP, and the setup is already written. You Don't Have To Pick There's an argument going around that MCP is the wrong way to extend an agent, and that command line tools and discoverable APIs are the Right Way. Half of that argument is correct, and it's the important half, so let's take it seriously. Dumping thirty tool descriptions into a context window is a bad way to teach anything. Not every Sprite command matters in every session, and cramming them all in signals to the model that they all matter to you. If you're not using network policies, gemini shouldn't burn a single token learning to configure them. Capabilities should reveal themselves progressively, the way they do when an agent works out a CLI one subcommand at a time. The wrong half is treating that as a case against MCP. Progressive disclosure is a question of what you say to the model. MCP is a question of how the bytes get there: transport, auth, structured results, a tool the model can call instead of a command whose flags it has to guess. Tho ## Build and deploy eve agents from the Vercel dashboard DevFeed: [Build and deploy eve agents from the Vercel dashboard]() Original publisher: [Read original article]() Author: Shar Dara Published: 2026-08-28T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Bots](), [AI Chat](), [MSP MCP]() Tags: [agents](), [ai](), [ai-gateway](), [code](), [data](), [git](), [identity](), [mcp](), [mcp-server](), [model](), [next-js](), [slack](), [tools](), [vercel](), [web]() ### AI overview Vercel's dashboard can now scaffold, repository-back, and deploy eve agents, with model selection, chat channels, and service integrations. ### Source excerpt You can now create eve agents directly from the Vercel dashboard, giving you a deployed agent that you can chat with right away. The builder scaffolds the agent, creates a private Git repository with its files, and deploys it as a new Vercel project on your team. In a few clicks, you get a working agent, backed by a repo with code you can use to customize it. From the builder you can: Write the instructions that define the agent's identity Pick any model available through AI Gateway Add a Next.js web chat or Slack channel where people can talk to the agent Give the agent tools and data from other services through built-in connections like Linear and Notion, or a custom MCP server Select Add New -> Agent in your dashboard to create your first agent. Read more ## Enterprise-managed auth for the Supabase MCP server DevFeed: [Enterprise-managed auth for the Supabase MCP server]() Original publisher: [Read original article]() Author: Cemal Kılıç; Greg Richardson Published: 2026-08-24T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [MSP MCP](), [AI Chat]() Tags: [ai](), [auth](), [identity](), [management](), [mcp](), [oauth](), [onboarding](), [security]() ### AI overview Supabase has made enterprise-managed authentication for its MCP server generally available on Team and Enterprise plans. Okta-based centralized access in Claude applies each employee's existing Supabase roles and permissions. ### Source excerpt Enterprise IT admins can now manage access to Supabase in Claude centrally through Okta, scoped to each person's existing permissions. ## How Cloudflare detects MCP traffic and helps secure it DevFeed: [How Cloudflare detects MCP traffic and helps secure it]() Original publisher: [Read original article]() Author: Kenny Johnson Published: 2026-08-14T13:12:12Z Content type: release Language: en Sources: [Cloudflare Blog]() Topics: [MSP MCP](), [AI Chat]() Tags: [agentic-ai](), [agents](), [ai](), [api](), [cloudflare-one](), [mcp](), [product-news](), [sase](), [security](), [zero-trust]() ### AI overview Cloudflare announces Cloudflare One controls for identifying MCP traffic, finding shadow connections, and enforcing Portal-only access to trusted MCP servers on managed network paths. ### Source excerpt Cloudflare Gateway identifies MCP requests using protocol-level heuristics. Security teams can use that signal to find shadow MCP traffic, enforce Portal-only access for approved servers, and block direct connections on managed network paths. ## Agent platform (Part 1): How we help Grab build and run AI agents at scale DevFeed: [Agent platform (Part 1): How we help Grab build and run AI agents at scale]() Original publisher: [Read original article]() Author: Kendrick Tan Published: 2026-07-24T00:00:10Z Content type: article Language: en Sources: [Grab Tech]() Topics: [AI Bots](), [AI Chat](), [MSP MCP]() Tags: [ai-agents](), [ai-infrastructure](), [architecture](), [design](), [engineering](), [experiment](), [generative-ai](), [go](), [llm](), [machine-learning](), [mcp](), [platform](), [scale]() ### AI overview Grab describes the first part of its AI-agent platform story: a Tech Infra support bot that evolved from operational automation into a GPT-powered Level-0 support layer. The article outlines its reasoning and tool planes and explains how scaling challenges informed the LLM-Kit framework. ### Source excerpt Part 1: From one support bot to a framework At Grab, AI agents have evolved from interesting team prototypes into production services used every day by millions of merchants, drivers, and consumers. Today, more than 500 services run on our internal agent framework, over 50 Model Context Protocol (MCP) servers are registered on our remote MCP framework, and a single Large Language Model (LLM) gateway fronts every model call across the company, handling billions of tokens each month. None of this was designed up front. It began as the plumbing behind one internal support bot, which then expanded because the same problems kept resurfacing for every team trying to ship an agent. This series tells the story of what the platform eventually became. This Part 1 of the blog focuses on the beginning: the architecture of our AI support bot, the specific pain points we hit while scaling and iterating on it, and how each of those failures became a core building block in the framework we now call LLM-Kit. The bot that started it Imagine you have a question for the Technical Infrastructure (Tech Infra) team - the engineers who run the cloud platforms, databases, developer tooling, and AI infrastructure behind Grab's ecosystem. Instead of immediately paging an on-call engineer, a bot first triages the request, checks the team's documentation, runbooks, and past Slack threads, and tries to answer directly in the thread. If it still cannot resolve the issue, it routes the ticket to the right human, with the relevant context already attached. That is what we built with the Tech Infra Support Bot. In the first half of 2023, Tech Infra handled thousands of support tickets, many of them repeated questions that had already been answered somewhere internally. Before LLMs, the bot's role was mainly operational; performing tasks like helping track acknowledgments and response times for on-call engineers. With the arrival of GPT-4-32k, we evolved it into a GPT-powered Level-0 support layer th ## Testing MCP Servers With a Python MCP Client DevFeed: [Testing MCP Servers With a Python MCP Client]() Original publisher: [Read original article]() Author: Real Python Published: 2026-07-07T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [MSP MCP]() Tags: [cli](), [command-line](), [mcp](), [mcp-server](), [python](), [servers](), [testing](), [tools](), [tutorial](), [video]() ### AI overview A video course on building a minimal Python MCP command-line client to test MCP servers over stdio, inspect capabilities, and work with tools, prompts, and resources. ### Source excerpt Learn how to build a Python MCP client that tests MCP servers from your terminal. List their tools, prompts, and resources, then call each one. ## Introducing eve, an open-source agent framework DevFeed: [Introducing eve, an open-source agent framework]() Original publisher: [Read original article]() Author: Timo Lins Published: 2026-06-17T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Chat](), [MSP MCP](), [Boilerplate]() Tags: [agents](), [announcement](), [development](), [github](), [mcp](), [open-source]() ### AI overview Vercel announces eve, an open-source framework in public preview for building and running agents. It uses a file-based agent structure and includes durable execution, sandboxed compute, approvals, subagents, and evaluations. ### Source excerpt eve is now available in public preview. eve is an open-source framework for building, running, and scaling agents. An agent is just a directory of files, and production comes built in: Durable execution Sandboxed compute Human-in-the-loop approvals Subagents Evals The smallest agent that runs is just two files, a model and a set of instructions. Add a tool, skill, channel, or schedule by adding a file. eve picks them up at build time and wires them in for you, so there's no boilerplate to register them. You can scaffold and start a new agent with a single command. It installs the dependencies, scaffolds the project, and starts a dev server, so you have an agent running locally in under a minute. Or you can let your coding agent set it up for you. Give it this prompt: And because an eve agent is an ordinary Vercel project, vercel deploy ships it to production unchanged, exactly as it ran on your machine. eve is the framework that Vercel builds and runs its own agents on. For the full tour, read the announcement or the documentation, and you can follow development in the open over at github.com/vercel/eve. Read more ## 1Password + Kiro: Trusted Access for AI-Powered Development DevFeed: [1Password + Kiro: Trusted Access for AI-Powered Development]() Original publisher: [Read original article]() Author: info@1password.com (Dennis Kromhout van der Meer and Scott Lougheed) Published: 2026-06-17T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Developer Tools](), [MSP MCP](), [AI Bots](), [AI Chat]() Tags: [ai](), [ai-security](), [coding](), [developers](), [kiro](), [mcp](), [secrets]() ### AI overview 1Password announces an integration between its MCP Server and Kiro that gives AI development workflows scoped, runtime credentials without exposing raw secrets in the model context. ### Source excerpt AI agents now write code, fix bugs, and ship to production. But in order to do useful work, agents require credentials. At 1Password, one of our core AI security principles is that raw credentials should never be directly exposed to LLMs, but all too often, that's exactly what happens: most teams sacrifice security for speed and hand agents secrets in plaintext. The shortcuts behind that tradeoff predate agentic development: secrets packed into an .env file, a script with hardcoded keys, a config file committed to a repo. But agentic development exponentially increases the blast radius of an exposed or misused credential, since anything in an agent's context window can be logged, echoed into output, or surfaced by an agent that's been manipulated into revealing it. Solving this tension means giving agents the access they need without directly exposing credentials to the model context. Today we're expanding the 1Password MCP Server to Kiro, making 1Password the trusted access layer for AI-powered development in Kiro. With this integration, credentials are issued at runtime, scoped to the assigned task, and never enter the model's context window. Agents get the access they need, and secrets stay where they belong. What this integration does Kiro is a software development agent, available in IDE, CLI or web experiences, bringing engineering rigor to AI-native coding via spec driven development and property-based testing. This integration connects Kiro directly to 1Password Environments using a local MCP server packaged inside our Password Manager developer tools. It's available on 1Password business and personal accounts. With this integration, developers can: Configure environments securely inside Kiro. Ask Kiro to create and configure an environment, then run applications using secrets stored in 1Password Environments instead of plaintext .env files, all without leaving Kiro. Clean up hard-coded secrets. Have Kiro find credentials in source code, move them into 1Pass ## Agentic Resource Discovery: Let agents search DevFeed: [Agentic Resource Discovery: Let agents search]() Original publisher: [Read original article]() Author: ben burtenshaw; shaun smith Published: 2026-06-17T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Bots](), [MSP MCP]() Tags: [agentic](), [agents](), [announcement](), [api](), [artificial-intelligence](), [compliance](), [hub](), [hugging-face](), [manifest](), [mcp](), [mcp-server](), [open](), [resource](), [search](), [skills](), [tool](), [tools]() ### AI overview The article introduces Agentic Resource Discovery (ARD), a draft open specification for cataloging and searching agent capabilities across federated registries. It describes Hugging Face's Discover Tool as a reference implementation that lets agents discover skills, ML applications, and MCP servers dynamically. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Laravel AI Agents Now Support MCP Servers DevFeed: [Laravel AI Agents Now Support MCP Servers]() Original publisher: [Read original article]() Author: Pushpak Chhajed Published: 2026-06-09T17:40:24Z Content type: release Language: en Sources: [Laravel Blog]() Topics: [MSP MCP](), [AI Bots]() Tags: [ai](), [ai-agents](), [auth](), [http](), [integration](), [laravel](), [mcp](), [model-context-protocol](), [oauth](), [tool](), [tools]() ### AI overview Laravel AI Agents can now connect to MCP servers through an MCP client and a thin Laravel AI SDK integration. ### Source excerpt Connect any Laravel AI agent to an MCP server using the Laravel AI SDK and MCP. Supports bearer and OAuth auth, STDIO and HTTP transports, and built-in caching. ## Meet Nuxi DevFeed: [Meet Nuxi]() Original publisher: [Read original article]() Published: 2026-06-09T00:00:00Z Content type: release Language: en Sources: [The Nuxt Blog]() Topics: [AI Chat](), [MSP MCP]() Tags: [agent](), [ai](), [article](), [developer-experience](), [docs](), [github](), [mcp](), [playground](), [ui]() ### AI overview Nuxt announces Nuxi, a context-aware AI companion for nuxt.com. It uses the Nuxt MCP server to ground answers in relevant documentation and other Nuxt resources, and adds interactive cards, StackBlitz playgrounds, saved conversations, and chat forking. ### Source excerpt Today, we're announcing the new Nuxt Agent: Nuxi. We want to make your Nuxt experience less generic and more personalized, with the care that characterizes the Nuxt community. ## 1Password is now a trusted access layer for OpenAI's Codex DevFeed: [1Password is now a trusted access layer for OpenAI's Codex]() Original publisher: [Read original article]() Author: info@1password.com (Dennis Kromhout van der Meer and Robert Menke) Published: 2026-05-20T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [AI Bots](), [MSP MCP]() Tags: [ai](), [ai-coding](), [codex](), [developers](), [mcp](), [mcp-server](), [openai](), [secrets](), [security]() ### AI overview 1Password describes an MCP integration for Codex that issues task-scoped credentials just in time while keeping secret values out of code and model context. ### Source excerpt Coding agents like Codex are helping developers write, execute, and prepare code for production. Every action that AI coding agents take against a database, an API, or a deployment pipeline requires access to credentials. Today, these credentials typically live in .env files, scripts, or hardcoded in repositories, where they can be easily exfiltrated and are difficult to govern and audit. The shift from AI assistance to AI execution has outpaced how teams manage the secrets needed for execution. 1Password and OpenAI are working together to close this gap. The 1Password Environments MCP Server for Codex makes 1Password the trusted access layer for Codex: credentials are issued just-in-time and scoped to the task, while keeping them outside the model's context window. Developers get the access they need to build and ship, while secrets stay where they belong. The same integration helps catch secrets at the source. Codex can be prompted to use 1Password and the 1Password MCP to store and use credentials that it needs. Why secrets should stay out of prompts, code, and model context Every credential placed inside an agent's context is a credential at risk of easily being exfiltrated. It can be logged, cached, reused across sessions, or surfaced in unexpected outputs. A secure architecture treats a coding agent as a tenant, not a vault: it gets secure access to do its job, but never custody of the secret itself. 1Password Environments is built on that principle. Instead of sharing .env files or hardcoding credential values, teams work from a shared environment where secrets are made available at runtime to the application, without the values ever appearing in code, terminals, or model context. This secure access model is built on the same vault technology and security architecture used across 1Password. Secrets remain end-to-end encrypted and centrally managed, with access limited to authorized users and groups, and through custom permissions. This architecture matters mo ## Deploy a Laravel MCP App to Production with Laravel Cloud DevFeed: [Deploy a Laravel MCP App to Production with Laravel Cloud]() Original publisher: [Read original article]() Author: Ana Tavares Published: 2026-04-17T13:55:54Z Content type: tutorial Language: en Sources: [Laravel Blog]() Topics: [MSP MCP](), [AI Chat]() Tags: [ai](), [authentication](), [chatgpt](), [claude](), [cloud](), [database](), [deployment](), [git](), [laravel](), [mcp](), [monitoring](), [production](), [saas](), [stripe](), [testing](), [tools](), [workflow]() ### AI overview A tutorial for building and deploying a Laravel MCP app on Laravel Cloud. It uses a SaaS support workflow in which Claude accesses typed, role-gated tools for customer subscription and invoice data, with production deployment covered alongside authentication, testing, error handling, and monitoring. ### Source excerpt Build and deploy a Laravel MCP app on Laravel Cloud. We cover tool implementation, error handling, authentication, testing, and production monitoring. ## Secure MCP credentials with 1Password and Runlayer DevFeed: [Secure MCP credentials with 1Password and Runlayer]() Original publisher: [Read original article]() Author: info@1password.com (Jeff Malnick) Published: 2026-03-20T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [MSP MCP]() Tags: [agents](), [ai](), [ai-agents](), [identity](), [integration](), [mcp](), [policy](), [security]() ### AI overview 1Password and Runlayer describe an integration that keeps machine credentials in 1Password vaults for MCP platforms, resolves them at runtime, and audits credential fetches and rotations without exposing secrets. ### Source excerpt We built 1Password® Unified Access to extend identity security beyond humans to the agents and machine workloads operating across your business. In practice, that means securing not just who gets access, but how agentic systems connect to tools, services, and data. That makes the MCP gateway a critical control point. It sits between AI agents and the systems they need to reach, making it the natural place to enforce policy, visibility, and governance. But in many deployments, it also becomes the place where credentials accumulate, moving secrets out of the vault and into the platform. That is the problem 1Password and Runlayer are solving together. With this integration, enterprises can keep their machine credentials in 1Password, resolve them only at runtime, and audit every fetch and rotation without exposing the secret itself. If your team has adopted an MCP platform to centralize how AI agents access tools, you've probably solved one problem and created another. Before the MCP platform, credentials were scattered across developer machines in plaintext config files: After the MCP platform, those credentials shifted from the developer's laptop into the platform's database. This centralizes them, but still keeps them outside your vault. Exposure on the local machine decreases, while secrets sprawl and operational complexity increase. A better model is to keep these credentials alongside the rest of your secrets, in a system that is consistent, easy to use, and supports self-service for the AI builder. MCP platforms shouldn't become another place for secrets sprawl Enterprises are using hundreds to thousands of upstream server connections like GitHub, Slack, Notion, Linear, and internal APIs. Each server needs at least one credential. When those credentials live outside the vault: A platform compromise exposes every MCP server token at once. There's no single source of truth for who changed what credential and when. Enforcement of credential access policies lives ou ## Laravel MCP Server: Auth & Security Best Practices DevFeed: [Laravel MCP Server: Auth & Security Best Practices]() Original publisher: [Read original article]() Author: Laravel Team Published: 2026-03-13T17:28:13Z Content type: tutorial Language: en Sources: [Laravel Blog]() Topics: [MSP MCP](), [cursor]() Tags: [api](), [auth](), [best-practices](), [cursor](), [http](), [laravel](), [mcp](), [mcp-server](), [oauth](), [security]() ### AI overview The article explains why Laravel MCP servers exposed over HTTP need authentication and describes OAuth 2.1 requirements such as PKCE, dynamic client registration, and endpoint metadata. It says the laravel/mcp package registers the related endpoints with one method call. ### Source excerpt Add authentication to your Laravel MCP server without writing an authorization server. Plus, security best practices. ## Laravel MCP: A Complete Guide DevFeed: [Laravel MCP: A Complete Guide]() Original publisher: [Read original article]() Author: Ana Tavares Published: 2026-03-12T15:00:30Z Content type: tutorial Language: en Sources: [Laravel Blog]() Topics: [MSP MCP](), [AI, ML & Data Engineering](), [cursor](), [GitHub Copilot]() Tags: [agent](), [ai](), [ai-agents](), [ai-coding](), [anthropic](), [api](), [chatgpt](), [claude](), [cli](), [code](), [cursor](), [database](), [github-copilot](), [guide](), [how-to](), [laravel](), [laravel-boost](), [mcp](), [mcp-server](), [tools]() ### AI overview A guide to building a Laravel MCP server that exposes application tools, resources, and prompts to compatible AI clients. ### Source excerpt Learn how to build a Laravel MCP server with tools, resources, and prompts. Step-by-step guide to exposing your app to AI clients like Claude and ChatGPT. ## Giving OpenClaw a memory that actually works DevFeed: [Giving OpenClaw a memory that actually works]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2026-02-23T00:00:00Z Content type: tutorial Language: en Sources: [Turso Blog]() Topics: [AI-assisted coding](), [MSP MCP](), [AI Bots]() Tags: [agents](), [claude-code](), [codex](), [coding](), [embeddings](), [local](), [mcp](), [mcp-server](), [memory](), [openclaw](), [retrieval](), [sqlite](), [turso](), [vector]() ### AI overview A tutorial for adding memelord's persistent, weighted memory system to OpenClaw through an MCP server. It uses local SQLite storage, locally generated embeddings, and Turso vector search to retain useful corrections and let stale memories fade. ### Source excerpt Add persistent, self-improving memory to OpenClaw in five minutes with memelord, an MCP server backed by Turso's local vector search over SQLite. ## Run AutoMCP To Supercharge Your AI Agent with Libraries MCP Servers DevFeed: [Run AutoMCP To Supercharge Your AI Agent with Libraries MCP Servers]() Original publisher: [Read original article]() Author: Liran Tal Published: 2025-12-03T05:00:00Z Content type: tutorial Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [MSP MCP](), [Vulnerabilities](), [cursor](), [Node.js]() Tags: [agentic-coding](), [ai](), [ai-agent](), [application-security](), [awareness](), [blog](), [ci-cd](), [developer](), [devrel](), [devsecops](), [engineering](), [javascript](), [mcp](), [mcp-server](), [node-js](), [sbom](), [scm](), [security](), [snyk-code](), [snyk-container](), [snyk-iac](), [snyk-open-source](), [snyk-platform](), [vs-code](), [vulnerabilities]() ### AI overview A tutorial on using AutoMCP to configure Model Context Protocol servers for AI coding tools, connecting dependency context and Snyk scanning to AI-assisted development. ### Source excerpt Supercharge your AI agent! Learn how AutoMCP integrates Model Context Protocol (MCP) servers and Snyk Studio for secure, context-aware AI-driven development. ## Streamlining Security Investigations with Agents DevFeed: [Streamlining Security Investigations with Agents]() Original publisher: [Read original article]() Author: Dominic Marks Published: 2025-12-01T16:00:42Z Content type: article Language: en Sources: [Engineering at Slack]() Topics: [AI Chat](), [MSP MCP]() Tags: [agents](), [ai](), [ai-agents](), [coding](), [data](), [design](), [development](), [engineering](), [events](), [infrastructure](), [manifest](), [mcp](), [mcp-server](), [performance](), [security](), [series](), [slack](), [software-engineering](), [uncategorized]() ### AI overview Slack Security Engineering describes an AI-agent prototype for investigating security alerts. To improve consistency and control beyond prompt refinement, the team decomposes investigations into chained model invocations with defined purposes and output structures, and exposes selected data sources through an stdio MCP server. ### Source excerpt Slack's Security Engineering team is responsible for protecting Slack's core infrastructure and services. Our security event ingestion pipeline handles billions of events per day from a diverse array of data sources. Reviewing alerts produced by our security detection system is our primary responsibility during on-call shifts. We're going to show you how we're using AI... ## MCP Safety Cheatsheet DevFeed: [MCP Safety Cheatsheet]() Original publisher: [Read original article]() Author: Ryan Vogel Published: 2025-11-19T19:44:35Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [MSP MCP]() Tags: [agent](), [ai](), [api-keys](), [database](), [dev](), [ide](), [llm](), [mcp](), [mcp-server](), [product](), [production](), [safety](), [sql](), [tools]() ### AI overview A safety checklist for operating MCP servers, focused on development-only access, least-privilege tools, read-only modes, human approval, secret handling, and usage visibility. ### Source excerpt MCPs are powerful abstractions, but that power also means risk. A misconfigured or overly permissive MCP server can expose sensitive data, execute unintended actions, and simply ruin your day. We maintain an MCP server at Neon, but we want you to use it safely. Here's a quick che... ## Introducing the Turso Database MCP DevFeed: [Introducing the Turso Database MCP]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2025-08-27T00:00:00Z Content type: release Language: en Sources: [Turso Blog]() Topics: [MSP MCP]() Tags: [ai](), [claude](), [claude-code](), [cli](), [database](), [mcp](), [mcp-server](), [sql](), [sqlite](), [turso]() ### AI overview Turso Database adds a built-in MCP server that lets AI assistants query data, modify schemas, and perform database operations through natural-language interactions. ### Source excerpt Turso Database now ships a built-in Model Context Protocol server. Add the --mcp flag and let AI assistants query and modify your SQLite databases. [Next page]()