“The Model Context Protocol is more than a bridge between machines and data, it is a bridge between human imagination and technological reality. Through it, intelligence becomes not just artificial, but actionable, accountable, and aligned with our highest purpose.” – MJ Martin
Introduction
The Model Context Protocol, commonly known as MCP, is an emerging open standard that defines how large language models and other forms of artificial intelligence interface with external systems. Its purpose is to unify how models gain access to real-time data, external tools, and specialized services that exist beyond their static training environments. In simpler terms, MCP provides a universal bridge between artificial intelligence and the many applications, databases, and digital infrastructures that power modern computing. Through this connection, an intelligent agent can not only reason and generate text but also take meaningful actions in the real world, such as retrieving information, executing commands, or managing workflows.
Traditionally, every artificial intelligence deployment required custom engineering to connect models to external data or applications. Developers had to design and maintain bespoke integrations for each use case. This practice created inefficiency, duplication, and compatibility issues. MCP resolves these problems by providing a single standardized communication layer. Its structure defines how an agent requests information, how that request is transmitted, and how the external system responds. It creates a “plug-and-play” environment similar to the way USB standardized the connection between computers and peripheral devices. As a result, MCP has been described as the “universal port” for intelligent systems.
What is it? – The Model Context Protocol is like a universal translator for artificial intelligence. Just as a skilled interpreter allows people who speak different languages to understand one another, MCP enables AI systems to communicate seamlessly with databases, applications, and digital tools that were never designed to “speak” to them. Without it, each connection would require a custom translation; with it, every system can share information through a common language of understanding.
Importance of MCP
The importance of MCP lies in the shift from passive to active artificial intelligence. Early conversational systems simply generated text responses. Today’s models are expected to act autonomously, connecting to external applications, running calculations, or managing user data safely. Without a shared protocol, every company must build its own bridge to each data source. MCP lowers this barrier to innovation by allowing developers to connect once and operate universally across models that support the protocol.
Standardization also drives modularity. Once an MCP connector, or “server,” has been written for a particular tool, it can be reused by other agents and clients. This reduces software redundancy and accelerates the growth of a connected ecosystem. The effect is similar to how the internet expanded once standard communication protocols such as HTTP were adopted. MCP could become the digital backbone that allows large language models from different providers to interact with a shared catalogue of tools, from cloud storage and databases to workflow automation and robotics.
MCP also supports responsible governance and security. Because it defines a clear, auditable interface between the model and the tool, it becomes possible to monitor, log, and control every action the model takes. Enterprises can assign permissions, apply access policies, and track usage in ways that were previously unmanageable in unstructured integrations. This feature will be critical as governments and corporations seek transparent and accountable artificial intelligence systems that can be trusted in sensitive environments.
Alex Albert (Anthropic, head of developer relations)
“Instead of maintaining separate connectors for each data source, developers can now build against a standard protocol.”
Practical Use of MCP
MCP is not theoretical. It is already being implemented by several organizations, notably by Anthropic, one of the developers of the Claude family of large language models. A typical deployment involves an MCP server connected to a specific data source or application, and an MCP client embedded in the artificial intelligence agent. The client can discover the server, authenticate, send a structured request, and receive a structured response. This process follows the rules of the protocol, ensuring predictability and safety.
For example, if a user asks an agent to “update the quarterly revenue spreadsheet and email the results,” the agent could connect through MCP to a spreadsheet server, retrieve the data, perform calculations, and then connect to an email server to send the report. Each of these steps occurs within a standardized transaction format defined by MCP. The same agent could then reuse the spreadsheet server for other tasks, such as forecasting or visualization, without requiring new integrations.
MCP has already inspired research benchmarks and practical experiments. One benchmark, called MCP-Bench, connects models to dozens of MCP servers spanning domains such as finance, travel, and academic research. The benchmark tests whether a model can plan and execute complex multi-step workflows using the protocol. Enterprise deployments are also being tested in secure environments where MCP servers are protected by identity management and audit controls. These real-world trials confirm that MCP can operate within the boundaries of modern cybersecurity frameworks and compliance regulations.
Anna Gutowska (IBM, AI Engineer / Developer Advocate)
“You can think of MCP for AI applications to serve the same purpose as a USB-C port serves for hardware.”
Advantages of MCP
The advantages of MCP are numerous. Its first and most obvious strength is efficiency. Developers no longer need to write separate adapters for every integration. By using MCP, they can design one connector that multiple agents or organizations can share. This efficiency lowers the cost of innovation and reduces the technical burden on small teams seeking to deploy intelligent systems.
Second, MCP encourages reuse and interoperability. When multiple agents follow the same communication protocol, they can operate across the same ecosystem of tools. This interoperability enables competition, creativity, and innovation because new participants can join the ecosystem without negotiating proprietary interfaces.
Third, MCP supports reliable orchestration of multi-step workflows. Agents can plan complex sequences of actions, knowing that the format and semantics of each step are consistent. This reliability reduces errors that might arise from misaligned data schemas or ambiguous commands.
Fourth, the protocol facilitates transparency and accountability. Because all tool usage occurs through defined endpoints, each action can be logged, audited, and verified. This makes MCP particularly valuable for enterprise and governmental use, where regulatory oversight is essential.
Fifth, the protocol enhances security through isolation. Each server can enforce its own access control, rate limits, and permissions. Instead of giving an agent unlimited access to internal systems, organizations can expose only the minimal necessary capabilities through MCP endpoints. This principle of least privilege limits potential harm if an agent misbehaves.
Finally, MCP provides a foundation for future growth. As artificial intelligence continues to evolve, new tools, sensors, and data streams will emerge. The existence of a standardized communication layer ensures that these new components can join the ecosystem without rewriting the entire infrastructure. MCP thus offers technological longevity and scalability.
Kevin Scott (CTO, Microsoft)
“It means that your imagination gets to drive what the agentic web becomes, not just a handful of companies that happen to see some of these problems first.”
Disadvantages and Risks
Despite its promise, MCP presents challenges and risks. The foremost concern is security. Because MCP allows artificial intelligence systems to take real-world actions, a malicious or compromised MCP server could exfiltrate data or perform harmful operations. Strong authentication, encryption, and sand-boxing are essential to prevent misuse.
Another difficulty lies in trust and governance. Since MCP servers can be developed by third parties, users must trust that these servers behave as advertised. Verifying the safety and integrity of every connector may prove challenging, particularly in open ecosystems where servers can be freely shared.
Implementation complexity is also a limitation. While MCP defines the structure of communication, it does not eliminate the need for rigorous design of access control, error handling, and compliance features. Enterprises must still invest engineering effort to ensure robust security and reliability.
Maturity is another issue. Because MCP is a new standard, the number of available connectors and compatible clients remains limited. Broader adoption will take time, and early users may face evolving specifications and compatibility issues. Version management, schema updates, and performance tuning are ongoing engineering tasks that require sustained attention.
Performance overhead may also occur. Abstraction layers inherently add latency and processing cost. In high-frequency or real-time applications, this overhead could limit responsiveness. Moreover, excessive use of MCP in trivial cases may complicate simple workflows that do not require the full protocol.
Finally, governance of the standard itself will be critical. To succeed, MCP must remain open, well-maintained, and free from vendor lock-in. Fragmentation of competing versions could weaken the ecosystem and erode interoperability, undermining the very benefits MCP seeks to provide.
Hao Song, Yiming Shen, Wenxuan Luo, et al. (security researchers, from a peer-reviewed preprint)
“The client-server integration architecture inherent in MCP may expand the attack surface against LLM Agent systems, introducing new vulnerabilities that allow attackers to exploit by designing malicious MCP servers.”
Summary
The Model Context Protocol represents an important step in the evolution of artificial intelligence from static reasoning systems to dynamic, action-oriented agents. It transforms the relationship between models and the external world by offering a safe, structured, and auditable means of interaction. Through MCP, artificial intelligence gains the ability to connect, act, and integrate responsibly across countless systems while maintaining trust and oversight.
Yet, as with all transformative technologies, MCP introduces new responsibilities. Security, governance, and interoperability must be carefully maintained to prevent misuse and ensure stability. If the community succeeds in balancing innovation with accountability, MCP may become one of the defining protocols of the artificial intelligence era, much as HTTP defined the web and TCP/IP defined the internet. Its success will depend not only on technical adoption but also on the human wisdom with which it is implemented.
Vikram Ekambaram (AI technologist / commentator)
“Anthropic MCP is the closest to something that I can build an autonomous agent with (without being a programmer).”
Endnotes
- “Model Context Protocol (MCP) Overview.” Anthropic Documentation. https://docs.anthropic.com/en/docs/mcp
- “Model Context Protocol (MCP) FAQ.” ModelContextProtocol.io. https://modelcontextprotocol.io/faqs
- “What Is Model Context Protocol (MCP)?” IBM Think Blog. https://www.ibm.com/think/topics/model-context-protocol
- “Understanding MCP and Agentic AI.” Cloudflare Learning Center. https://www.cloudflare.com/learning/ai/what-is-model-context-protocol-mcp/
- “MCP and the Future of Agentic Workflows.” Anthropic Newsroom. https://www.anthropic.com/news/model-context-protocol
- “Enterprise Security for Model Context Protocol.” arXiv preprint arXiv:2504.08623. https://arxiv.org/abs/2504.08623
- “MCP-Bench: Evaluating Tool Use and Workflow Planning.” arXiv preprint arXiv:2508.20453. https://arxiv.org/abs/2508.20453
- “Adopting MCP Today: Pros and Cons.” GetKnit Blog. https://www.getknit.dev/blog/the-pros-and-cons-of-adopting-mcp-today
- “Evaluating the Model Context Protocol.” WillowTree Apps Blog. https://www.willowtreeapps.com/craft/is-anthropic-model-context-protocol-right-for-you
- Simon, Julien. “The Truth About MCP: Pros, Cons, and Real-World Use Cases.” Medium, 2025. https://medium.com/@julsimon/the-truth-about-mcp-pros-cons-real-world-use-cases-2e51bbec7219
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees, in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 60 next generation MOOC (Massive Open Online Courses) continuous education in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.