{"id":179,"date":"2026-06-01T12:39:58","date_gmt":"2026-06-01T12:39:58","guid":{"rendered":"https:\/\/solucom.si\/?p=179"},"modified":"2026-06-02T14:56:05","modified_gmt":"2026-06-02T12:56:05","slug":"mcp-model-context-protocol","status":"publish","type":"post","link":"https:\/\/solucom.si\/en\/mcp-model-context-protocol\/","title":{"rendered":"AI Agents: How They Connect to Your Tools"},"content":{"rendered":"<p>You know that feeling when you try an AI assistant and think, \u00abIt answers well, but it knows nothing about my company\u00bb? That's the point. A model, on its own, knows how to talk. It doesn't know where you keep your clients, where your quotes are, or what the status of an order is. To let it know, you have to <em>connect it<\/em> to your tools. And here, until recently, the problems began: every connection was a custom piece of software, expensive to build and fragile to maintain.<\/p>\n<p>MCP was born to solve exactly this. It's worth understanding it now, because in the coming years it will become the normal way AI agents connect to the tools you use every day. But precisely because it provides access to your data, it should be introduced methodically.<\/p>\n\n<h2>What is MCP, simply put<\/h2>\n<p>MCP stands for <strong>Model Context Protocol<\/strong>. It is an open standard presented by <strong>Anthropic in November 2024<\/strong> to uniformly connect AI models to external tools and data. Think of it like a standard outlet: instead of building a different plug for each tool, you use just one that works for all of them. But the metaphor ends there, because what passes through the outlet isn't electricity: it's <strong>actions<\/strong> e <strong>data<\/strong> of your company.<\/p>\n<p>The fact that it's not a fleeting trend is confirmed by a concrete fact: <strong>December 2025 Anthropic donated MCP to the Agentic AI Foundation<\/strong>, a fund under the Linux Foundation created alongside OpenAI and Block. Translated: it is no longer a single company's protocol, but shared industry infrastructure. And it's already supported by ChatGPT, Claude, Gemini, Microsoft Copilot, and several code editors.<\/p>\n\n<h2>Why is it different from a classic custom integration<\/h2>\n<p>Before MCP, every connection was a job in itself. Did you want your assistant to read the CRM? Dedicated integration. Even from Drive? Another integration. Did you change AI models? You often had to start over. It's a problem that grows quickly: many models for many tools mean many combinations to build and maintain.<\/p>\n<p>MCP overturns logic: it defines a <strong>common language<\/strong>. Write the link to a tool <em>once<\/em>, in the form of \u00abMCP server,\u00bb and any agent that speaks MCP can use it. Less throwaway code, more reusable pieces. For you, running a business, the practical consequence is simple: connections become cheaper and less tied to a single vendor.<\/p>\n\n<h2>The pieces of the puzzle, without jargon<\/h2>\n<ul>\n<li><strong>Client MCP<\/strong> \u2014 it's the app or the agent making the requests: ChatGPT, Claude, an assistant within your management software.<\/li>\n<li><strong>Server MCP<\/strong> \u2014 it's the bridge to a single tool (your CRM, Drive, management software). It sits in the middle and translates.<\/li>\n<li><strong>Tool<\/strong> \u2014 the <em>actions<\/em> that the server makes available: \u00absearch customer\u00bb, \u00abcreate draft response\u00bb, \u00abopen ticket\u00bb.<\/li>\n<li><strong>Resources<\/strong> \u2014 I <em>data<\/em> that the server can read: a document, a customer card, the list of orders.<\/li>\n<\/ul>\n<p>You don't need to remember the names. The idea is enough: the client asks, the server acts as a bridge, the tools are the things that can be done, and the resources are the things that can be read.<\/p>\n\n<h2>A practical example<\/h2>\n<p>Imagine an email from a client: \u00abCan you confirm the timing and price for the supply we discussed?\u00bb Here's what happens with an agent connected via MCP:<\/p>\n<ul>\n<li>the agent uses the <strong>CRM MCP server<\/strong> to find the customer card and history;<\/li>\n<li>use the <strong>MCP server on Drive<\/strong> to search for the last linked quote;<\/li>\n<li>collate the information and prepare a <strong>draft response<\/strong> with timelines and figures consistent with those documents;<\/li>\n<li>Leave the draft to you: read, correct if necessary, send.<\/li>\n<\/ul>\n<p>The important point is the last one. The agent doesn't ship anything on their own: they prepare, you approve. The same pattern applies to a <a href=\"\/en\/rag-explained-simply\/\">System that fishes from your documents<\/a> to answer without making things up.<\/p>\n\n<h2>What changes, in practice<\/h2>\n<p><strong>For those building AI systems<\/strong> (You or those who work for you): Less time spent reinventing connections, more time on logic that adds value. A well-written MCP server for your CRM can be reused for the customer assistant, the quote generator, and internal support. Changing the AI model underneath? The connections remain. It's a change in mindset: you stop building \u00abdisposable\u00bb integrations and start building lasting components.<\/p>\n<p><strong>For whom uses the system<\/strong> (you, your collaborators, clients sometimes): the difference is immediately noticeable. The assistant stops responding blankly and starts giving you answers based on <em>yours<\/em> Real data: the status of an order, the correct quote, the history of that customer. Not a generic chatbot, but something that knows your company\u2014within the limits you've set.<\/p>\n\n<h2>When MCP makes sense<\/h2>\n<ul>\n<li>Usi <strong>more tools<\/strong> and you want the AI to see them all without building a different integration for each one.<\/li>\n<li>Translate <strong>more use cases<\/strong> Over time: today customer responses, tomorrow quotes, the day after tomorrow support.<\/li>\n<li>Do you want to stay <strong>freed from the single supplier<\/strong> AI: If you change the model, the links remain.<\/li>\n<li>You already work with clients who speak MCP (now the majority).<\/li>\n<\/ul>\n\n<h2>When it's excessive<\/h2>\n<ul>\n<li>Hi <strong>a single, simple, and stable connection<\/strong>Sometimes a direct API call is all you need.<\/li>\n<li>You are a small team <strong>no time to handle permissions and checks<\/strong>It is better to start extremely small than to open ten doors and not oversee them.<\/li>\n<li>The use case is \u00abone-off\u00bb: if you do it only once, you don't need infrastructure.<\/li>\n<\/ul>\n\n<h2>Risks not to underestimate<\/h2>\n<p>Let me be clear, because this is the part I see overlooked most often. An MCP server, in fact, is something that acts <strong>on your own<\/strong> inside your systems. So:<\/p>\n<ul>\n<li><strong>Permissions<\/strong>Give each server only what it needs. If it needs to read the CRM, don't give it permission to delete as well.<\/li>\n<li><strong>Sensitive data<\/strong>Decide in advance what can be seen and what cannot. An agent should not access payroll or personal data if their job is to respond to customers.<\/li>\n<li><strong>Third-party server<\/strong>: Installing an MCP server found online is like hiring someone without references and giving them the keys. Only use reliable sources, especially for real data.<\/li>\n<li><strong>Security<\/strong>: token with expiration, minimum access, logs of agent's actions, and human approval for important actions.<\/li>\n<\/ul>\n<p>The rule I use: <strong>Treat an MCP server as a collaborator with the office keys<\/strong>. Would you give a newcomer the keys to everything on the first day? No. Same criteria.<\/p>\n\n<h2>Common errors<\/h2>\n<ul>\n<li>Connect <strong>all the tools together<\/strong> on the first try, instead of starting from one.<\/li>\n<li>Grant <strong>total permissions<\/strong> \u00abTo avoid problems\u00bb: that's exactly how problems arise.<\/li>\n<li>Use <strong>unverified public servers<\/strong> directly on production data.<\/li>\n<li>Don't hold <strong>track<\/strong> of actions and not predict a point where a person controls.<\/li>\n<\/ul>\n\n<h2>The first, concrete step<\/h2>\n<p>Don't start with the infrastructure. Start with a <strong>solo instrument<\/strong> and from a <strong>single controlled use case<\/strong>. Realistic example: connect the agent to the CRM <strong>in read-only<\/strong>, with the sole task of preparing draft responses. Minimal permissions, one person to approve, and for two weeks you observe what they do. If it works and you trust it, you expand: a second tool, a second use case. One step at a time.<\/p>\n<p>If you already automate something today, know that tools like <a href=\"\/en\/n8n-automation-saves-hours\/\">n8n introduced native MCP nodes<\/a>You can have your flows interact with agents without writing everything from scratch.<\/p>\n\n\n<p>MCP is not another acronym to chase. It's how, more and more often, a <a href=\"\/en\/what-is-an-ai-agent\/\">AI agent<\/a> stop \u00abjust talking\u00bb and start doing useful things within your tools. The technology is there and it's mature; the difference is made by <em>come<\/em> Introduce it: bit by bit, with strict permissions and one person at the helm.<\/p>\n\n\n<h2>Frequently Asked Questions<\/h2>\n\n\n<p><strong>Do you want an agent that actually *does* things in your tools, not just responds?<\/strong> Let's start with the right connection\u2014just one, and done safely. Tell me which tool you use most in your company, and I'll tell you where it's best to start: <a href=\"\/en\/#contatti\">Write us a couple of lines.<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>What is the Model Context Protocol, why is it different from a custom integration, and how to securely connect an AI agent to your tools, starting with just one.<\/p>","protected":false},"author":1,"featured_media":212,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_seopress_titles_title":"MCP spiegato semplice: agenti AI e i tuoi strumenti | Solucom","_seopress_titles_desc":"L'MCP e lo standard che fa parlare gli agenti AI con i tuoi software. 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