The Role of Model Context Protocol in AI

March 24, 2025


Key Insights (TLDR)

  1. What MCP Is: Model Context Protocol (MCP) is an open standard developed by Anthropic to connect AI assistants with external data sources and tools. It standardizes how AI systems access external context and perform actions.
  2. The Problem MCP Solves: Traditional AI integration requires custom code for each data source or API, making connections brittle and hard to scale. MCP provides a standardized way for AI models to discover and use external tools without hardcoded integrations.
  3. Recent Adoption Surge: While Anthropic announced MCP in November 2024, it only gained significant traction in early 2025 because:
    • It addresses the critical integration problem for agentic AI systems
    • A growing ecosystem has developed
    • It's becoming a de facto open standard compatible with any AI model
  4. MCP vs. Previous Approaches:
    • Custom API Integrations: Separate code is required for each service
    • Language Model Plugins: They are proprietary and limited to specific platforms
    • Framework Tools (LangChain): Custom implementation is required for each tool
    • RAG: Provides static text but not interactive capabilities
  5. Limitations: Challenges include:
    • Multiple tool servers require management overhead
    • Uncertainty about cloud-based scaling
    • Dependency on quality tool descriptions
    • Evolving standards that may lead to breaking changes
    • Security considerations
  6. MCP's Role in Agentic Systems: MCP focuses on agents' "Action" component, providing standardized connections to the external world. It complements rather than replaces orchestration tools like LangChain.
  7. Emerging Applications:
    • Multi-step workflows across different systems
    • AI agents that understand their environment (IoT, smart homes)
    • Collaborative multi-agent systems sharing standard tools
    • Personal AI assistants with deep but secure integration into personal data
    • Enterprise governance and security for AI tool access

MCP represents a significant advancement in making AI systems more integrated with external data and tools, transforming them from isolated systems to versatile agents that can interact effectively with the world. For a deeper dive, please continue.

I. Introduction to Model Context Protocol (MCP)

Main Points:

MCP is an open standard developed by Anthropic that serves as a bridge between AI assistants and external data sources, tools, and systems. It establishes a standardized way for AI models to discover, connect to, and interact with resources beyond their training data, whether querying databases, accessing file systems or calling APIs.

Unlike previous approaches that required custom code for each integration, MCP provides a unified language for AI systems to communicate with external tools, eliminating the need for brittle, one-off connections.

The key innovation of MCP lies in its dynamic discovery capabilities, allowing AI agents to automatically detect available MCP servers and their functionalities without hardcoded integrations. This creates a flexible ecosystem where developers can create specialized MCP servers (connectors) for different tools, and any MCP-compatible AI can immediately use them through a consistent interface.

MCP enables AI systems to become more context-aware and capable of performing complex, multi-step tasks across different systems and data sources—effectively turning AI from isolated "brains" into versatile "doers" that can interact meaningfully with the digital world.

II. Why MCP Is Making Waves

Main Points:

There are several key reasons why MCP gained popularity in early 2025 rather than immediately after its November 2024 announcement: When initially released, MCP was seen as an exciting concept but didn't immediately capture widespread attention. The AI community was still primarily focused on agents, model capabilities, and prompt engineering rather than integration challenges. MCP represented a solution to a problem many hadn't yet recognized as critical: how to systematically connect AI agents with external systems and data sources in a standardized way. By early 2025, several factors converged to propel MCP into prominence:

First, as agentic AI systems became more mainstream, their integration limitations became painfully apparent.

Second, a network effect took hold as the ecosystem expanded rapidly to make the protocol more valuable with each new integration.

Third, Anthropic actively improved MCP and provided educational resources, including a viral workshop at the AI Summit that accelerated adoption.

Finally, MCP's open, model-agnostic approach positioned it as a de facto standard that could work across different AI platforms, giving it an advantage over proprietary solutions in the increasingly interconnected AI landscape.

III. Traditional Approaches to AI Integration Before MCP

Here are the traditional approaches to AI integration that existed before MCP:

1. Custom API Integrations (One-off Connectors)
The most common method was writing custom code or using SDKs for each service individually, thus creating a fragmented approach.

2. Language Model Plugins (OpenAI Plugins)
This approach provided models with standardized plugin specifications to call external APIs in a controlled way. However, plugins typically focus on one-way data retrieval rather than maintaining interactive sessions.

3. Tool Use via Frameworks (LangChain tools, Agents)
Agent orchestration libraries like LangChain popularized giving models tools with descriptions like search() or calculate(). While powerful, each tool still required custom implementation. LangChain's library grew to 500+ tools with a consistent interface, but developers still needed to configure those tools to their specific needs. The standardization was at the developer level, not the model level.

4. Retrieval-Augmented Generation (RAG) and Vector Databases
Supplying context to LLMs typically involves searching a knowledge base and injecting top results into the prompt. This addresses knowledge limitations with static text snippets but doesn't allow the model to perform actions beyond what was indexed. RAG provides passive context, whereas MCP enables active fetching or acting on context through defined channels.

Each approach had limitations MCP could address, particularly the lack of standardization and the challenge of scaling integrations across multiple systems.

IV. MCP's Role in Agentic Orchestration

Main Points:

MCP plays a specific and vital role in agentic AI systems but with clear boundaries. It is not an agent framework but a standardized integration layer for agents. It focuses primarily on the "Action" component of autonomous agents - providing a consistent way for AI systems to interact with external data and tools.

Autonomous agents typically need several building blocks: Profiling (identity and context), Knowledge, Memory, Reasoning/Planning, Reflection, and Action. MCP specifically addresses the Action part by standardizing how agents perform operations involving external systems.

Without MCP, developers would need custom integrations for each external system an agent interacts with. MCP complements rather than replaces agent orchestration tools like LangChain.

While these orchestration systems determine when and why an agent should use a tool, MCP defines how tools are called and information is exchanged.

By handling the "plumbing" that connects AI agents to the outside world, MCP allows developers to focus more on agent logic and capabilities rather than integration details, making agents more versatile and adaptable across different contexts.

IV. Limitations of MCP

Main points:

  1. Management Overhead: Running and maintaining multiple tool servers adds complexity, particularly in production environments. The overhead of managing these connections can be cumbersome.
  2. Cloud Scalability Concerns: MCP was initially designed for local and desktop use, raising questions about how well it translates to cloud-based architectures and multi-user scenarios.
  3. Tool Usability Issues: Having more tools doesn't guarantee effective use. AI models can struggle with tool selection and execution despite MCP's structured tool descriptions. Success depends heavily on the quality of these descriptions and the AI's ability to interpret them correctly.
  4. Technology Maturity: As a relatively new technology, MCP is subject to rapid changes and evolving standards. This can lead to breaking changes requiring frequent updates to servers and clients. Organizations need to prepare for version upgrades and evolving best practices.
  5. Limited Compatibility: While MCP has strong support within Anthropic's ecosystem, broader adoption across other AI platforms remains uncertain. Without native support from other AI providers, additional adapters or custom integrations may be required.
  6. Potential Overkill for Simple Use Cases: For straightforward applications needing only one or two simple API integrations, the complexity of implementing MCP might outweigh its benefits. Direct API calls could be more efficient in these scenarios.
  7. Security and Monitoring Challenges: Since MCP acts as an intermediary, it requires robust authentication and permission controls to prevent unauthorized access. Securing MCP in enterprise environments remains a work in progress.

None of these limitations are "show-stoppers," but users may need to start using MCP with non-critical deployments to gain experience with the technology.

V. New Possibilities Unlocked by MCP

  1. Multi-Step, Cross-System Workflows: MCP enables AI agents to seamlessly coordinate actions across multiple platforms.
  2. Environment-Aware Agents: MCP allows AI to interact with smart environments, including sensors, IoT devices, or operating system functions. This gives AI assistants real-time awareness of their surroundings, enabling proactive assistance for smart homes or computer systems.
  3. Collaborating Agent Systems: Specialized AI agents could use MCP to exchange information and coordinate tasks dynamically, accessing a common toolset without needing direct integrations with each other.

These are only early glimpses of MCP's potential.

VI. Concluding Thoughts

In this article, we explored the fundamentals of MCP, highlighting its role in optimizing AI-driven workflows. We examined its key benefits, including enhanced adaptability, improved efficiency, and modular scalability, which allow AI models to dynamically interact with different computational modules.

Additionally, we discussed MCP's limitations, such as potential integration challenges, increased system complexity, and computational overhead due to multi-module coordination.

Despite these challenges, MCP represents a significant leap forward in AI development. Traditionally, AI systems functioned as isolated "brains" focused on pattern recognition and decision-making. However, MCP has the potential to transform AI from a passive information processor into an active and adaptable "doer" that seamlessly integrates reasoning, execution, and learning across multiple domains. This shift could redefine AI workflows, enabling more autonomous, efficient, and interactive systems that bridge the gap between intelligence and action.

MCP could revolutionize robotics, digital assistants, and enterprise automation, empowering AI to perform complex, multi-step tasks with greater precision and adaptability. By evolving from static models to dynamic, modular AI frameworks, MCP paves the way for the next generation of versatile, real-world AI solutions that can intelligently navigate and execute tasks across diverse environments.