Is the Model Context Protocol MCP a missing standard in AI infrastructure?

The explosive growth of artificial intelligence, especially large language models (LLM), has revolutionized the way businesses operate, from automated customer service to enhanced data analytics. However, as enterprises integrate AI into their core workflows, an ongoing challenge arises: how to securely connect these models to data sources in the real world without custom, decentralized integration. The Model Context Protocol (MCP), introduced by Anthropic in November 2024, is a potential solution, an open standard designed to act as a universal bridge between AI agents and external systems. MCP is often expected to standardize connections compared to USB-C’s plug-in potential, allowing models to access fresh, relevant data on demand. But, can we really reshape the missing standards of AI infrastructure? This in-depth article explores the origins, technical work, strengths, limitations, real-world applications, and future trajectories that draw on insights from industry leaders and early implementation as of mid-2025.

The Origin and Evolution of MCP

The development of MCPs stems from the basic limitations in AI systems: their isolation from dynamic enterprise-level data. Traditional LLM relies on the generation of pre-trained knowledge or retrieval functions (RAG), and the company often involves embedding data into vector databases, a process that is computationally intensive and prone to taints. Anthropic recognizes this gap by using MCP as an open source protocol to facilitate a collaborative ecosystem. Adoption accelerates by early 2025 when competitors like Openai integrate it, demonstrating a broad industry consensus.

The protocol is based on the client server model, its open source SDK languages such as Python, TypeScript, Java, and C# to facilitate rapid development. Pre-built servers for tools like Google Drive, Slack, Github, and PostgreSQL allow developers to quickly connect to datasets, while companies like Block and Apollo have customized them to proprietary systems. This evolution positions MCP as a proprietary tool, but as a base layer, similar to HTTP-standardized Web communications, with the potential to enable proxy AI, i.e. autonomously autonomously on data rather than just the system that handles it.

Detailed mechanism: How MCP works

MCP operates at the core of a structured bidirectional architecture that ensures secure data exchange between AI models and external sources. It includes three key components: an MCP client (usually an AI application or proxy), an MCP host (routing request), and an MCP server (connecting to a tool or database).

Step by step process

1. Tool Discovery and Description: The MCP client sends descriptions of available tools to the model, including parameters and patterns. This allows the LLM to understand possible operations, such as querying the CRM or executing code segments.
2. Request a routing: When the model decides an operation (e.g., retrieving customer data from a Salesforce instance), the host converts it into a standardized MCP call. It uses protocols such as JWT or OIDC to ensure only authorized access.
3. Data retrieval and verification: The server fetches the data, applies custom logic (e.g., error handling or filtering), and returns structured results. MCP supports real-time interaction without pre-index, reducing latency compared to traditional rags.
4. Context integration and response: The retrieved data is fed back to the model, and the model generates a response. Functions such as context validation prevent illusions by grounding the output in the verification information.

This workflow remains stateful across interactions, allowing complex tasks such as creating GitHub repositories, updating databases, and notifying via Slack sequence. Unlike rigid APIs, MCP adapts to the probability properties of LLMS by providing flexible patterns, thus minimizing failed calls due to parameter mismatch.

Advantages: Why MCP may be a missing standard

The design of MCP addresses several pain points in the AI infrastructure, providing tangible benefits for scalability and efficiency.

• Seamless interoperability: Through standardized integration, MCP eliminates the need for customized connectors. Enterprises can expose various systems (from ERP to knowledge base) as MCP servers that can be reused between models and departments. This repeatability accelerated deployment, with early reports showing integration times in pilot projects 50% faster.
• Increased accuracy and hallucination reductionLLMS usually reacts when it lacks context; MCP refutes this by providing precise real-time data. For example, in legal queries, the hallucination rate drops from 69-88% in ungrounded models to near zero and has a proven context. Components such as context validation ensure that the output is consistent with corporate truths, thereby increasing trust in areas such as finance and health care.
• Strong security and compliance: Built-in executors provide granular controls such as role-based access and data repair to prevent leaks, which is the focus of 57% of consumers. In regulated industries, MCP helps comply with GDPR, HIPAA and CCPA by retaining data within enterprise boundaries.
• Scalability of proxy AI: MCP enables development of no code or low-proxy agents to democratize AI by non-technical users. The survey shows that 60% of enterprise planning agents adopted it within a year, and MCP facilitates multi-step workflows such as automated reporting or customer routing.

Quantitative benefits include lower computational costs (avoiding vector embeddings) and improve ROI with fewer integration failures.

Real-world applications and case studies

MCP has proven its value across industries. In financial services, it is based on LLM in proprietary data for accurate fraud detection, thereby reducing errors by providing a compliant, real-time environment. Healthcare providers use it to query patient records without exposing PII, ensuring HIPAA compliance while achieving personalized insights. Manufacturing companies use MCP for troubleshooting, extracting from technical documents to minimize downtime.

Early adopters (such as Reply sourceGraph) integrate it into a context-aware encoding where the agent accesses a real-time code base to generate functional output with less iterations. Block uses MCP to implement a proxy system that automates creative tasks and emphasizes its open source spirit. These cases highlight the role of MCP in the transition from experimental AI to production-level deployment, with more than 300 companies adopting a similar framework by mid-2025.

What the future means: moving towards a standardized AI ecosystem

As AI infrastructure reflects the complexity of multi-cloud, MCP may become the key to hybrid environments, facilitating collaboration similar to cloud standards. With thousands of open source servers and integrations from Google and others, it’s ready. However, success depends on mitigating risks and promoting governance through community-driven improvements.

All in all, MCP represents a key advancement that bridges the isolation of AI from real data. While not flawless, the potential of standardized connectivity makes it a strong candidate for missing standards in AI infrastructure, enhancing more reliable, scalable and secure applications. As the ecosystem matures, companies that adopt it early may gain a competitive advantage in an increasingly stable world.