The world of AI and large language models (LLMs) is advancing rapidly. Still, real, reliable intelligence remains limited when these systems operate in a silo, isolated from the information and systems around them. Until recently, connecting an AI assistant to live data (be it APIs, databases, or enterprise content) meant laborious, bespoke integrations. Every new data source required another brittle connector, making ongoing maintenance a headache and unscalable. This all changes with the arrival of the Model Context Protocol (MCP): an open standard—reliable and secure—redefining how AI models interact with the world.

Before MCP standards, connecting LLMs and AI assistants to business data or digital tools felt like forcing mismatched plugs into a socket: fragile, improvised, and rarely futureproof.. While API function calls let LLMs reach out, there wasn’t a harmonized way for developers, or the models themselves, to understand and reliably talk to new services. 

Now, imagine you’re building a tool, and instead of wrestling with a tangle of custom integrations every time you want your AI to fetch real, up-to-date context and live data or perform an action, you plug in with ease, just like using USB-C on your laptop. That’s the vision behind MCP, being the link that was missing: a shared language for AI-to-tool communication that brings clarity instead of chaos.

What is MCP? A universal connection that redefines how AI gets things done

API function calling enabled some interaction between LLMs and external tools, but there was no universal language for these integrations. Each connection was bespoke, limiting scalability and stifling innovation. Developers were trapped in cycles of patchwork integrations that hindered speed and reliability.

MCP working as connector between AI apps and tools, databases, resources, etc.

MCP solves this by acting as the translator for AI: a single, unified, and agnostic protocol that lets AI systems securely plug into any tool, database, or resource, regardless of vendor or architecture. Now, developers and organizations can adopt one standard, making it effortless for any LLM or agent to access the necessary data and actions. With MCP, it no longer matters how many apps, databases, or systems you want your AI to tap into, since they are formed by modular, interchangeable connectors.

Anatomy of MCP: host, client, and server

MCP is built around a streamlined, modular architecture composed of three primary roles:

  • MCP host (AI Application): The ‘brains’ of the operation, where the AI lives. Whether it’s an AI-powered application, an agent platform, or an orchestration framework, the host contains the integration logic, manages sessions and permissions, and coordinates the workflow to decide how and when to pull in data or trigger tools.
  • MCP client: This component sits within the host and acts as a “walkie-talkie”. It handles communication, in a 1:1 connection, between the host and each specific server, converting the host’s requests into structured protocol messages that the server understands. Likewise, the client receives responses or notifications from the server and translates them back to the host.
  • MCP server: This simple, reusable, lightweight service exposes a specific data source or tool, like Slack, GitHub, or a database, through MCP’s standardized endpoints. Servers can provide resources (read-only information retrieval), tools with actions (external APIs calling to perform a calculation, send a message, or fetch live data), or prompts (pre-defined prompt templates or workflows to facilitate complex interactions), all discoverable and composable by the host. The server receives structured requests from the client, executes the appropriate action (like querying an API or database), and returns a structured result back to the client.

Think of it as an AI assistant asking for your store’s stats: the host hears the request, the client shuttles it over, and the server delivers the answer, all via standardized, secure messaging. MCP uses JSON-RPC 2.0 for structured, self-descriptive messaging, and both local (STDIO) and remote (HTTP with Server-Sent Events) integrations are supported, making MCPs highly adaptable.

Key features and principles

At Empathy.co, our commitment to contextual, human-centric AI aligns perfectly with MCP’s key features. By adopting MCP within our AI innovations, we’re laying the groundwork for standardization, modularity, and security with the following principles as a core:

  • Universal standard interface: MCP defines a single protocol for integrating any tool: API, database, or service, removing the need for custom integrations per tool-model pair. This promotes interoperability and scalability across systems.
  • Open ecosystem: As an open-source, vendor-neutral protocol, MCP can be used with any LLM and lowers barriers for new connectors, tools, and workflows, fueling collaborative AI innovation with no restrictions.
  • Reusable server-side integrations: Build once, reuse everywhere. MCP servers are modular, reusable connectors, dramatically reducing duplicated effort and simplifying development. 
  • Composability, modularity, and extensibility: Tools are designed as small, independent servers that can be combined in workflows. Thanks to the shared protocol, they interact seamlessly and evolve independently from the clients and hosts. This way, new capabilities can be added without breaking existing implementations.
  • Security and isolation: Each server only sees what’s necessary, nothing else. The host enforces strict boundaries, securing sensitive data and simplifying compliance, which prevents unintended data leaks between tools.
  • Two-way communication: Beyond handling requests, MCP allows servers to send asynchronous messages and even trigger model responses (for example, summarizing retrieved data), which enables richer interactions.

Milestones of leveraging MCP

Being honest, an AI that only knows what it was trained on isn’t truly helpful in a fast-moving world. Here’s why MCP is changing the game for everyone building, using, or trusting AI:

  • Smarter answers, grounded in now: No more guessing or outdated responses. MCP lets AI assistants dip into live data, such as your store’s inventory, yesterday’s emails, or the status of “project X”, instead of just what they remember from training. Because they can check facts in real time, your AI’s answers get sharper, more relevant, and more trustworthy since LLMs’ context awareness avoids hallucinations and irrelevant answers.
  • From conversation to action: Imagine telling your AI to send a message, book a meeting, or crunch those numbers, and it just gets it done. MCP transforms AI from a clever talker into a full-on helpful agent. With easy connections via MCP tool calls, LLMs cannot only answer questions but carry out mutli-step, end-to-end duties, perform actions, automate tasks in an autonomous or semi-autonomous way, and take real steps—saving you time and mental energy.
  • Developer-friendly by design: Old-school integrations required custom code for every tool and every update. MCP keeps things neat: one protocol, lots of tools. That means less time wrangling code, easier updates, and swapping in new apps or models without breaking the whole experience. For anyone building with AI, that’s faster development cycles and easier maintenance.
  • Reliability and robustness: MCP acts as a middle layer, adding a level of fault-tolerance and consistency in tool usage. If something goes wrong, like a tool is offline or data isn’t in the right shape, MCP has error handling built in. Your AI can keep its cool, try alternatives, or let you know what’s up, all without missing a beat. That’s reliability you can count on.
  • Cross-tool and multi-agent coordination: When multiple tools or AI agents need to cooperate, like in chain reactions where one action leads to another, MCP keeps everyone in sync. By speaking a shared “language” since all tools are accessed through the same protocol, different agents and tools can pass information, coordinate, and build on each other’s work. No more lost-in-translation moments, just smooth collaboration.
  • Privacy, security, and compliance control: Sensitive data stays in your court. If your AI needs insights from private sources, MCP lets you keep those connections in-house, without involving third-party AI services, ensuring compliance, auditability, and granular control over access and approvals. This limits what the AI is allowed to do, and logs everything for peace of mind.
  • Collaborative future: Best of all, MCP is more than a technical fix, it’s fostering the creation of a new shared ecosystem for AI connectivity. Like how USB standardized device connections or HTTP created the open web, MCP aims to create an AI “plug-and-play” world where innovation multiplies in a common layer. As more teams adopt MCP, the community grows, integrations increase, and everyone benefits.

At Empathy, we believe connections between people, data, and ideas are at the heart of better technology. MCP is helping make those connections smarter, quicker, safer, and more empathetic. Thus, it isn’t just a protocol; it’s a bridge between AI’s perception and practical execution, thus aligning insight with action. Because MCP is open and composable, innovation can move faster, experiments can go and evolve further without losing track and control of all of these evolutions.

Exploiting the unlimited potential of MCP 

MCP is already transforming real-world scenarios and inspiring new, more dependable use cases across industries. Whether it’s a multi-agent team working on a complex project, a retrieval-augmented assistant feeding your LLM up-to-the-minute insights, or a business process streamlined for both merchants and customers, MCP makes it possible:

  • Multi-agent collaboration on complex business workflows that use shared MCP-connected tools, which promotes consistency and coordination, and reduces hallucinations, especially when agents operate on a single source of truth. 
  • Real-time retrieval-augmented generation (RAG) for smarter, up-to-date answers that allow more strategic, flexible use of knowledge sources.
  • Streamlined integration of AI on modular workflows that assist developers with code repositories, issue trackers and testing, CI/CD systems, and documentation.
  • Seamless business process automation—AI managing emails, calendars, and messaging.
  • Elevated customer support thanks to service agents, with instant access to inventory, CRM, and order management tools to resolve support queries in real time.

Revolutionizing Backroom with AI

We’re excited to announce our plans to implement MCP within our Backroom tool for Motive and Empathy Platform customers. Imagine an AI assistant dedicated to helping merchants, product managers, and analysts see the full picture of their business, not just analyze metrics. The roadmap for the MCP implementation in Backroom follows this thread: 

  • Short term: Seamlessly surface and explain analytics data directly within Backroom with an AI assistant that explains and visualizes this data in plain language.
  • Medium term: Offer proactive recommendations based on internal stats and merchant goals. An AI assistant that guides you on how to get the best of your business by boosting products, updating stock, and refining campaigns.
  • Long term: Empower the assistant to execute actions automatically, always with your previous consent. It could adjust campaigns, update product data, or schedule reports thanks to MCP-powered integrations with Backroom’s API.

We see MCP as more than tech; it’s the connective tissue for an ecosystem where AI works for you, not the other way around. As its adoption accelerates and our AI agents evolve, Empathy.ai will deliver experiences that are not only smarter but fundamentally more empathetic, envisioning a future where technology understands both your context and your goals.