The Model Context Protocol (MCP) and Its Future Potential

Let's now discuss about one of the most promising technology I saw in the last few months The Model Context Protocol (MCP).

is an open standard designed to unify and standardize how AI systems—especially large language models (LLMs)—interact with external tools, APIs, and data sources. In few words let's talk and go in deep in how AI interacts with the real world.

Introduced in late 2024, MCP serves as a state management and communication layer allowing AI agents to autonomously decide which tools to use, how to chain tool executions, and how to maintain persistent context across multi-turn interactions.

MCP Analogy: In essence, MCP is to AI what HTTP is to the web—a universal interface that enables interoperability, scalability, and secure integration across diverse AI tools and services.

What MCP Solves Today

Prior to MCP, AI integrations were highly fragmented, requiring custom connectors and workflows for each tool or API, which hampered scalability and introduced security vulnerabilities. MCP addresses these challenges by:

Providing a modular, open standard protocol for AI-to-tool communication
Enabling persistent, structured context exchange to support stateful agent behavior
Supporting seamless interoperability among AI platforms and tools
Introducing built-in security and authentication frameworks to safeguard communications
Simplifying developer workflows with reusable, standardized API patterns

Major AI platforms like Claude and ChatGPT Enterprise have adopted MCP as a foundational integration layer, while developer tools increasingly support MCP natively to enhance AI-powered code authorship, debugging, and multi-modal workflows.

Remote MCP Server Access

It is important to note that running a local MCP server is not always necessary. MCP clients can connect to remote MCP servers hosted on trusted infrastructures, allowing seamless, scalable, and multi-tenant managed environments. Remote MCP server access usually involves standardized authentication mechanisms such as OAuth or API keys, and secure communication channels using TLS.

Remote MCP servers can offer advantages such as centralized tool management, improved reliability, and easier updates, while still providing users with flexible and real-time AI integration. This setup also allows organizations to delegate MCP hosting to specialized providers, reducing operational overhead and enhancing scalability.

Security Note: When connecting to remote MCP servers, it remains essential to confirm server authenticity and trustworthiness to avoid exposure to compromised or maliciously controlled servers. Network security controls, user access policies, and continuous monitoring play critical roles in maintaining a secure remote MCP environment.

Security Considerations for MCP Usage

Before using the Model Context Protocol (MCP), it is crucial to verify the trustworthiness of the MCP source or server being accessed. As MCP enables AI agents to dynamically interact with external tools and data, malicious actors can exploit this flexibility to embed harmful functionality inside apparently legitimate tools or to manipulate communication flows. Common security threats include:

Tool poisoning, where a seemingly harmless tool may execute malicious commands.
Unauthorized updates that transform legitimate tools into attack vectors.
Server spoofing, where fake MCP servers imitate valid ones to intercept data or inject harmful instructions.
Injection attacks and man-in-the-middle interceptions that tamper with MCP command payloads.
Data exfiltration through improperly secured structured contexts.

Due to these risks, strict source validation and security hygiene are mandatory. MCP workflows should implement robust authentication (e.g., OAuth with scope restrictions), encrypted TLS communication with mutual authentication, digital signatures on messages, and centralized logging and anomaly detection. Organizations should integrate MCP monitoring into their Security Operations Centers and ensure continuous staff training on MCP-specific incident response.

Emerging Developments and Future Evolutions of MCP

As of mid-2025, MCP is rapidly evolving from a promising standard to a mature, enterprise-ready infrastructure with broad industry adoption. Here are some key trends and future directions shaping MCP's evolution:

1. Enhanced Security and Authentication

Current MCP implementations often rely on session-level OAuth 2.1 authorization. Future versions aim to introduce more fine-grained authorization controls, allowing enterprises to manage user permissions and tool access more securely—potentially integrating Single Sign-On (SSO) and enterprise-managed authorization. Expanded best practice guides and validation tooling will improve developer security posture when deploying MCP servers.

2. Support for Asynchronous and Long-Running Operations

MCP is evolving to natively support asynchronous workflows that may run over extended periods, including resilient disconnection/reconnection handling. This will enable more complex, agent-driven scenarios such as multi-step task orchestration and workflows that span diverse systems.

3. Multimodality and Streaming

To keep pace with AI capabilities, MCP is expanding to support multiple data modalities beyond text—such as video, images, and interactive media—through multipart streaming and bidirectional communication. This will unlock immersive AI experiences integrating diverse sensor inputs and media channels.

4. MCP Registry and Marketplace

A centralized MCP registry service is in development to enable easier discovery, distribution, and versioning of MCP servers, akin to npm or API marketplaces. This will facilitate dynamic AI agent tool selection, enhancing flexibility and reducing manual integration overhead.

5. Remote-First, Multi-Tenant Architecture

While many existing MCP deployments are local or single-user, the protocol's future includes support for remote MCP servers with multi-tenant capabilities. This will allow SaaS-like environments where many users share MCP services securely, with isolated data and control planes, improving scalability and enterprise readiness.

6. Improved Developer Tooling and Ecosystem

A growing ecosystem of MCP-compatible server generation tools, hosting vendors, and connection managers aims to streamline development, deployment, and maintenance. Enhanced debugging support and compliance test suites will improve reliability and developer experience across client and server implementations.

7. Unified Workflow and UI/UX Patterns

Future MCP iterations may embed workflow management primitives, enabling agents to coordinate multi-step toolchains with built-in resumability and error handling. Standardized UI/UX patterns for tool invocation, discovery, and ranking will create more predictable and user-friendly MCP client experiences.

Let's imagine now the Future of MCP Usage

The Model Context Protocol (MCP) is set to transform AI application design far beyond current standards, enabling dynamic, context-aware ecosystems where AI agents seamlessly integrate with diverse tools and data sources. Below are expanded use cases, detailed examples, and ongoing developments illustrating this promising future.

Advanced Use Case Themes

Dynamic Tool Orchestration: MCP lets AI agents decide autonomously which tools to invoke and in what order, orchestrating complex workflows across multiple services with stateful context preservation.
Real-Time IoT Integration: Future MCP ecosystems will ingest real-time sensor data streams, enabling AI agents to respond intelligently to physical environments, smart buildings, or industrial automation.
Enterprise AI Mesh: MCP supports multi-tenant MCP servers offering centralized registries of tools, datasets, and prompts, enabling enterprises to securely manage AI resources and workflows organization-wide.
Multimodal Data Handling: Expanding beyond text, MCP will support video, images, and interactive content, fostering immersive AI applications in AR/VR, gaming, and advanced user interfaces.
Collaborative AI Agents: Multiple MCP-powered agents collaborating and handing off stateful workflows between each other to solve complex problems interactively.

Real-World Case Studies and Ongoing Developments

1. Microsoft: Enhancing Virtual Assistant Productivity

Microsoft's Cortana virtual assistant leverages MCP to access specialized task tools dynamically, improving user engagement by 20%. For example, Cortana uses MCP servers that provide calendar access, email handling, and contextual knowledge tools—allowing seamless, multi-step automation like scheduling meetings considering real-time availability and email content.

2. IBM: Improving Language Model Accuracy

IBM integrated MCP to connect language models with proprietary databases and external APIs, boosting the accuracy of language understanding by 10%. This integration enables domain-specific AI applications, such as legal contract analysis tools that pull in up-to-date regulations and case law dynamically.

3. Amazon Alexa: Expanding Virtual Assistant Capabilities

Amazon uses MCP to orchestrate diverse skill plugins for Alexa, allowing greater contextual awareness and multi-modal interaction. Alexa can now chain commands like booking tickets, setting reminders, and controlling smart home devices responsively by maintaining rich session state across tools.

4. Browser Automation with MCP Servers

The Browserbase MCP server enables LLMs to control and interact with web browsers in the cloud, powering applications that can navigate websites, take screenshots, and execute JavaScript. This capability is pivotal for automating research, monitoring, and testing workflows where AI-driven browsing and data extraction are required.

5. LutraLutra AI Agent

LutraLutra transforms conversations into actionable workflows by connecting to MCP servers that expose APIs of common business tools. Users can convert chat commands directly into automated email campaigns, report generation, and data entry tasks—streamlining repetitive processes with reusable automation playbooks shared across teams.

6. Open Source MCP Ecosystem Growth

The expanding MCP ecosystem is seen on platforms like GitHub, where over 30 MCP server implementations cover use cases including search integration, sequential reasoning, and cloud file operations. This diversity demonstrates growing community adoption and innovation, potentiated by comprehensive SDKs and tooling.

Looking Forward: Visionary Scenarios

"Everything App" Platforms: Future AI applications that fluidly combine communication, task management, creative work, and IoT control into a single MCP-powered interface.
Human-AI Collaboration: MCP enabling humans to seamlessly intervene mid-workflow, with agents pausing, awaiting guidance, or explaining decisions before continuing.
Monetized AI Tool Marketplaces: Dynamic MCP registries will allow paid individual tool access, fostering innovation and specialization in AI-assisted productivity.
AI in Smart Cities: Distributed MCP agents managing energy grids, traffic systems, public safety, and citizen services with coordinated, contextually-aware multi-agent workflows.

For us as technicians it is really important to stay updated on such kind of technology, it promises to shift how AI tools are built, consumed, and monetized, becoming a critical enabler for the next generation of intelligent applications.

The Model Context Protocol (MCP)

Vittorio Margherita