Before November 2024, a programmer wanting to connect the same tool—for example, a PostgreSQL database—with Claude , ChatGPT , and Gemini had to write three completely separate integration code snippets. Each model had a different tool calling format, a different JSON schema, and a different authentication mechanism. The result was hundreds of engineering hours wasted on purely "patchwork" work, generating no business value whatsoever.
This problem has a precise name: Data Silos plus Integration Tax. Each AI vendor creates its own closed ecosystem—OpenAI's Custom GPTs, Gemini's Extensions, Copilot's Skills—forcing developers to choose a playing field and sacrifice compatibility with the rest.
The Model Context Protocol ( MCP ) was created to put an end to this. Released as open source by Anthropic in November 2024, MCP is an open, standardized protocol that allows any AI model to connect to any tool or data source—through a single, consistent communication layer. If USB-C freed us from the mess of charging cables, MCP is doing the same for the world of AI integration.
The core role of MCP - Breaking the Plugin Monopoly of Big Tech
What is MCP in technical terms?
MCP is an application layer protocol built on JSON-RPC 2.0 – a lightweight, stateless communication standard that has been proven in production for over a decade. The MCP architecture consists of three components:
- MCP Host: User-side AI applications (Claude Desktop, ChatGPT app, Cursor IDE)
- MCP Client: An integrated component within the host that manages connections to servers.
- MCP Server: An independent service that exposes tools, resources, or prompts for AI to use.
The intermediate layer (Abstraction Layer) separates the LLM from the tool.
This is the most important architectural insight for understanding why MCP is important.
Before MCP, an AI agent had to "know" how to call each specific tool. OpenAI Function Calling required developers to register the schema directly in the API request. Gemini Extensions required separate configuration in Google AI Studio. None of those were cross-reusable between models.
MCP creates an abstraction layer between the two sides:
[ LLM / AI Model ] ←→ [ MCP Client ] ←→ [ MCP Server ] ←→ [ Database / API / File System ] Result: The LLM doesn't need to know whether you're using PostgreSQL or MongoDB, REST or GraphQL. It only needs to know that there's a tool with a name query_databaseand description. The MCP server behind the scenes handles the rest.
Solve the "Build Once, Run Anywhere" problem.
Before March 2025, if a company built an MCP Server for its internal tools, that server would only run on Claude. From March 2025, when OpenAI officially supports MCP in the Agents SDK and ChatGPT desktop app, that same server will immediately work on both Claude and ChatGPT. Google Gemini will follow suit in April 2025. Microsoft will integrate it into Copilot Studio in July 2025.
The formula changes from N×M to N+M:
- Before MCP: N models × M tools = N × M individual integrations had to be written and maintained.
- After MCP: N models + M MCP Server = N+M components, each component written once, arbitrarily linked.
This is precisely why the MCP SDK reached 97 million monthly downloads by March 2026, with over 10,000 publicly available MCP servers registered. The growth rate that the React npm package took 3 years to achieve, MCP accomplished in 16 months.
Three irreplaceable advantages of MCP architecture.
1. Maximum reusability; do not change a single line of code when changing models.
An MCP Server is defined using a standard JSON Schema that describes the tools, resources, and prompts it provides. This schema is not dependent on any specific AI vendor.
Practical example: A company builds an MCP server to connect to its internal CRM system in January 2025 for use with Claude. In April 2025, the team decides to try GPT-4o for some use cases. With MCP, the only thing to do is add client configuration in ChatGPT; no business logic needs to be rewritten.
This has significant strategic implications: Organizations are no longer locked into a single AI vendor. Investment in integration is completely independent of model choice.
2. Local-First Security - Data does not leave the device.
This is the least discussed but most important advantage for businesses.
MCP supports two transport layers:
- stdio (Standard Input/Output): Communication via stdin/stdout streams on the local machine. No packets go over the internet. Server and Client run on the same machine, connected via a direct pipe.
- Streamable HTTP (formerly SSE): Used for remote servers, supporting streaming over HTTP with Server-Sent Events.
When a company connects Claude to its internal codebase via AI stdio, the source code never leaves the local machine. The AI reads files, executes queries, and analyzes logs—all within the developer's machine's sandbox. This is something traditional cloud plugin solutions cannot guarantee.
For remote MCP servers in enterprise environments, the recommended security standards are:
- HTTPS with TLS 1.3
- OAuth 2.1 for token issuance and rotation
- Principle of Least Privilege: Each server only exposes the tools it needs (for example,
query_sales_datainstead of the full database admin). - Rate limiting and access control on each tool
3. Optimize the Context Window and save on Token costs.
Context windows are the most expensive resource in AI . Every token passed to the prompt costs money and increases latency.
The old integration method (transferring all data):
Prompt = System + Full Database Export (50.000 tokens) + User Question → Chi phí: $0.15/1K tokens × 50 = $7.50/request → Latency: 8-12 giây
MCP (on-demand fetching) way:
Prompt = System + User Question (500 tokens) → AI quyết định gọi tool: query_database(filter="relevant_rows") → Tool trả về 200 rows liên quan (2.000 tokens) → Chi phí: $0.15/1K × 2.5 = $0.38/request → Latency: 2-3 giây
On -demand fetching means that AI only retrieves data when it's truly needed, and only the relevant parts. In complex Agentic RAG workflows with multiple inference steps, the cost difference between the two approaches can be as much as 10-20 times.
Section 3: An Overview of the Race to Integrate MCP on Leading AI Platforms
Comparison table of MCP status across major platforms (May 2026)
Anthropic Claude - A pioneer, the architect of the standard.
Claude Desktop and Claude Code were the first platforms to fully implement MCP and be production-ready. Anthropic not only created MCP but also designed it vendor-neutral from the outset, a crucial strategic decision that has helped the entire industry follow suit.
Claude Code has a distinct advantage: developers can connect Claude to local filesystems, Git repositories, terminals, and databases stdiowithout worrying about data being sent outside. This is a key factor that makes Claude Code popular with security-conscious teams in enterprises.
In December 2025, Anthropic donated the MCP specification to the Linux Foundation through the Agentic AI Foundation (AAIF), transforming it from "Anthropic's open-source" standard into an "industry-independent standard." This is the most significant step in the protocol's development history.
Cursor IDE and AI Code Assistants - The Power of Plan Mode
Cursor is the clearest example of how MCP can transform the developer experience.
With MCP, Cursor's Plan Mode allows AI to plan and execute complex tasks by:
- Reading project structure via MCP File System Server
- Query the database to understand the schema before writing the migration.
- Run the test and read the output via the MCP Terminal Server.
- Search for dependency documentation via MCP Web Search Server.
As a result, an AI coding assistant can work within the real-world context of the project—not on hypothetical data or outdated training data.
OpenAI ChatGPT - A strategic shift from a walled garden.
In March 2025, OpenAI announced MCP support across its Agents SDK, Responses API, and ChatGPT desktop app. This was a pragmatic decision acknowledging that the developer community had built such a large MCP ecosystem that it couldn't be ignored.
Notably, Custom GPTs and Actions coexist with MCP. These two approaches serve different target audiences: Custom GPTs for end-users who don't need to code; MCP for developers who want to build production-grade agent systems.
Starting in September 2025, the full MCP implementation for ChatGPT has been rolled out, including a "Developer Mode" that allows connection to both local and remote MCP servers.
Google Gemini and Microsoft Copilot - The pressure to converge on common standards.
Google will integrate MCP into Gemini via Google AI Studio and Vertex AI in April 2025, allowing enterprise clients to connect their existing internal systems to Gemini without rewriting the integration.
Microsoft is moving in by July 2025 with Copilot Studio and VS Code, notably the VS Code extension with GitHub Copilot, which can now use the same MCP Server as Cursor, eliminating fragmentation in developer tooling.
The clearest indication of the future: After Anthropic awarded MCP to the Linux Foundation, pressure on Google and Microsoft to adhere to an independent standard increased significantly. Neither could create their own forks or variants without facing opposition from the developer community.
MCP and Agentic AI Architecture - The Infrastructure Layer of the Next Generation of AI
From Chatbot to Actionable Agent
The core difference between a chatbot and an AI agent lies in their ability to act in the real world . A chatbot answers questions. An agent completes tasks.
Without an MCP (or an equivalent standard), the agent can only act within the pre-installed ecosystem. With an MCP, the agent can connect to any system with an MCP server—from internal ERP systems and legacy databases to modern SaaS APIs.
Gartner forecasts that by the end of 2026, 40% of enterprise applications will integrate task-specific AI agents – up from less than 5% in early 2025. MCP is the foundational connecting layer for this entire growth wave.
Americ RAG: Integrating MCP with Knowledge Retrieval Pipeline
The most widely adopted pattern in enterprises in 2026 is the combination of MCP with Retrieval-Augmented Generation :
- The agent receives requests from the user.
- The agent calls the MCP Vector Store Server to retrieve the relevant documents.
- The agent calls the MCP Database Server to retrieve structured data.
- The agent combines context and inference.
- The agent calls the MCP Action Server to execute the result.
Each step in the pipeline is a separate MCP tool call. The entire pipeline works with Claude, GPT-4o, or Gemini without changing the logic.
Conclusion: The future of Artificial AI with open connectivity standards.
MCP accomplished what many previous connectivity standards failed to do: create industry consensus without forcing anyone to join.
The journey from 2 million SDK downloads (November 2024) to 97 million (March 2026), from an Anthropic project to a standard of the Linux Foundation – is not the journey of a marketing technology, but the journey of a solution that addresses a real-world problem that thousands of developers face every day.
In 2026 and beyond, MCP is shaping three major trends:
Firstly, there's the AI Marketplace plug-and-play – an ecosystem where enterprises buy MCP Servers like SaaS subscriptions, plugging them into any AI model they're using without needing custom development.
Secondly, in multi-agent orchestration, AI agents coordinate with each other via MCP, with each agent specializing in a domain and sharing context through shared MCP state.
Third, Compliance-ready AI – with detailed audit logs on each MCP tool call, enterprises can meet GDPR, SOC2, and other compliance requirements without sacrificing AI capabilities.
As a Senior AI Engineer once said: "MCP's first year transformed how AI systems connect to the world. Its second year will transform what they can accomplish." Looking at the current adoption rate, there's little reason to doubt that.