Header Banner Header Banner
Topics In Demand
Notification
New

No notification found.

Model Context Protocols: The Global Standard for Agentic Communication or a New Security Loophole?
Model Context Protocols: The Global Standard for Agentic Communication or a New Security Loophole?

35

0

As Agentic AI continues to evolve, numerous protocols are emerging that enable autonomous communication and coordination. These protocols enable agents to independently discover, select and allocate resources without requiring human intervention.

In an Agentic AI system, agents don’t just talk to each other by chance or through preset rules. As the system grows more complex, there’s a need for a structured way for agents to select and interact with the right resources dynamically. To solve the challenge of how AI agents communicate with external tools and resources, Anthropic on 25th November 2024 introduced the Model Context Protocol (MCP) as an open standard to connect AI assistants to the systems where data lives, including repositories, business tools and development environments. It provides context to Large Language Models (LLMs) and outlines the best practice to ensure data security within an organization’s own infrastructure while following a client server architecture where host application can connect to multiple servers.

Emerging need for MCP

Before MCP, developers faced the repetitive task of creating custom connectors for every combination of agents and tools, leading to what Anthropic referred to as an “N x M” integration problem, where each new tool or resource added exponential complexity. Traditional APIs operate on fixed, point-to-point interactions, requiring developers to manually code integrations for each agent-tool pair, without a standardized way to manage context or task flow.

As a result, developers had to build custom APIs and constantly glue together components for simpler tasks. This approach not only increased development effort but also created isolated legacy systems where information couldn’t flow freely across agents and systems.

MCP solves this by offering standard communication framework and eliminating isolated data systems and offering standard way for agents to communicate with diverse tools and legacy systems. With MCP Agents are no longer limited to simplify answering queries, they can now execute complex multi-step tasks such as fetching data, summarizing documents or saving content. MCP abstracts away the need for individual API wiring by providing a universal context-sharing layer, enabling intelligent, scalable, and autonomous agent-to-resource communication. Agents can send structured requests to any tool that supports MCP, receive real-time results, and even chain multiple tools in a workflow, all without requiring prior knowledge of each tool’s implementation details.

Adoption Landscape

Since its release, MCP has seen rapid adoption across AI ecosystem, with leading LLM providers, hyperscalers and technology giants embracing it as the preferred framework for agent-to-resource communication. Its open-source nature allows any organization to integrate MCP into their system. The table below highlights key companies and how they are integrating MCP into their ecosystems.

Company

Reason for Adoption

OpenAI

Openai Logo icon - Free Download PNG & SVG | Streamline

Integrated across its products, including the ChatGPT desktop app, OpenAI's Agents SDK, and the Responses API.

Replit

Integrated so that agents can read and write code across files, terminals, and projects.

Sourcegraph

Sourcegraph Software Pricing & Plans 2025: See Your Cost

Plugging it into dev workflows for smarter code assistance.

Codeium

Codeium | Discover AI use cases

Enhancing its compatibility across various development environments

Microsoft

Using in Copilot and making it easier for non-developers to connect AI to data and tools, no coding required

Azure

Standardize and enable AI models to interact with various Azure services and tools

Google’s DeepMind

Category:Google AI | Logopedia | Fandom

Supporting incoming Gemini models and related infrastructure

Table 1: Companies integrating MCP (Model Context Protocol)

Industry trends states that the AI ecosystem is moving towards standardized, open protocols for agent communication. Companies are adopting MCP not only to streamline but also to simplify their development workflows, standardize how AI model interact with external resources and enable agents to read, write and modify codes.

Early adoption risks and outlook

While MCP is gaining significant hype, it is important to recognize that both the protocol and AI technology itself are still evolving with each passing day. Companies across the ecosystem are rapidly adopting MCP in its early days. In this rush, the industry is moving through a blind tunnel, without fully addressing the protocol’s evolving security concerns. As the usage scales, the attack surface expands, introducing new forms of cyber security threats.

In April 2025, security researchers highlighted several critical vulnerabilities within MCP implementations. These included risks like prompt injection attacks, insufficient tool permission controls where chaining tools could lead to unintended data exfiltration and lookalike tools silently impersonating trusted resources to intercept sensitive information. Following this, a wave of additional MCP-related security vulnerabilities began to surface.

This implies that MCP also brings new threat surfaces that require urgent industry-wide attention before it can mature into a truly secure protocol. Although there is strong momentum behind MCP, achieving true “Global Standard”, status will depend on broader adoption, governance and security fixes.

Nevertheless, it is rapidly becoming the preferred protocol across AI ecosystem for enabling context-rich communication between agents and resources. Traditionally the security checks could be manually enforced by humans at multiple points in the workflow, providing a safeguard before critical actions were executed. However, as Agentic AI systems shift towards fully autonomous operations, even vulnerability assessments and compliance checks are increasingly being handled by agents themselves. This makes it even more important for the protocol itself to be strong and source, making it imperative that MCP’s security architecture evolves just as swiftly, ensuring it can autonomously safeguard these highly interconnected, self-regulating environments.


That the contents of third-party articles/blogs published here on the website, and the interpretation of all information in the article/blogs such as data, maps, numbers, opinions etc. displayed in the article/blogs and views or the opinions expressed within the content are solely of the author's; and do not reflect the opinions and beliefs of NASSCOM or its affiliates in any manner. NASSCOM does not take any liability w.r.t. content in any manner and will not be liable in any manner whatsoever for any kind of liability arising out of any act, error or omission. The contents of third-party article/blogs published, are provided solely as convenience; and the presence of these articles/blogs should not, under any circumstances, be considered as an endorsement of the contents by NASSCOM in any manner; and if you chose to access these articles/blogs , you do so at your own risk.


AI Analyst and Learner with a background in AIML and Data science

© Copyright nasscom. All Rights Reserved.