What is MCP (Model Context Protocol) ?

In-Depth Analysis of the Model Context Protocol (MCP) for Large Language Models

1. Definition and Conceptual Background of MCP

The Model Context Protocol (MCP) is a standardized communication protocol that connects AI applications based on large language models (LLMs) to external data and tools. In simple terms, it’s often compared to “USB‑C for AI,” enabling diverse AI agents (e.g., ChatGPT, Claude) to interact with external systems in a unified way.

Why is MCP needed?

Traditionally, LLMs were confined to their training data, lacking access to up‑to‑date information and the ability to interact with the real world in real time. In addition, each AI model often required a custom integration approach, so connecting a new data source or tool meant bespoke development every time. To address these limitations, Anthropic first proposed MCP in November 2024, and since then it has been establishing itself as the “standardized language” between LLMs and external systems. By adopting MCP, LLMs can evolve from static knowledge bases into dynamic agents capable of real‑time data retrieval and task execution.

Problems MCP aims to solve

MCP alleviates several chronic pain points in LLM application development:

• Model knowledge limitations: It mitigates the issue of LLM knowledge becoming stale post‑training. Through MCP, models can connect to real‑time data sources (e.g., news, databases) and reflect the latest information.
• Difficulty using external tools: There was no unified way to connect LLMs to tools like search engines, databases, or calculators; MCP provides a standard interface for this.
• Fragmented integrations: The need for one‑off, 1:1 integrations between each AI and each tool is simplified by MCP’s plug‑and‑play pattern. Build an MCP server once, and multiple AI models can use it via the same spec — reducing redundant engineering.
• Context switching challenges: When agents switch among multiple data sources or tools, maintaining coherent context is hard. MCP promotes an end‑to‑end, consistent context model that spans heterogeneous sources and tools.
• Scalability constraints: Bespoke, tightly coupled integrations are hard to scale. MCP decouples models from tools, easing expansion so new tools can be added and large‑scale integrations become straightforward.

In short, MCP is designed so LLMs can safely and in a standardized way use external knowledge and capabilities, which in turn boosts factual accuracy and the level of automation they can achieve.

2. Current Adoption and Concrete Usage

Since launch, MCP has seen rapid adoption across leading AI companies and the developer ecosystem. Anthropic, OpenAI, and Google have showcased MCP‑based integrations, while many open‑source projects and platforms now support MCP clients/servers. Representative examples include:

• Anthropic Claude: As the originator, Anthropic has adopted MCP aggressively in Claude. The Claude Desktop app can connect to local MCP servers to access personal data or integrate with internal systems, and Claude 3.5 (Sonnet) can even help generate MCP server code. Anthropic has also open‑sourced MCP connectors (servers) for Google Drive, Slack, GitHub, PostgreSQL, and other major systems, making it easy to plug into enterprise chatbots or coding agents.
• OpenAI ChatGPT: In 2025, OpenAI introduced MCP into ChatGPT. By enabling ChatGPT Developer Mode, users can connect third‑party MCP servers and tools directly to the ChatGPT web interface. This leapfrogs the prior closed plugin system, allowing developers to build their own MCP connectors and integrate them with ChatGPT. For example, a developer connected a Stripe MCP connector to check balances and issue invoices and then chained it with a Zapier connector to send the result to Slack, demonstrating multi‑step automation within a single chat. ChatGPT can automatically invoke connected MCP tools during conversations, and users can constrain it to specific connectors via prompt instructions. OpenAI also cautioned that while this is powerful, it’s risky, so it requires user confirmation for all write actions and warns about prompt injection, destructive commands, or malicious MCP servers. This official support further solidifies MCP’s status as an industry standard.
• Google Gemini: Google has actively connected MCP to its next‑generation multimodal LLM, Gemini. The 2025 Gemini Code Assist (an IDE‑integrated AI for developers) includes agentic chat that uses system tools and MCP servers to carry out complex, multi‑step tasks. Google also released an open‑source Gemini CLI, a terminal‑based agent that lets developers use the Gemini 2.5 Pro model from the command line, with built‑in support for extending functionality via MCP. In demos, Gemini CLI gathered information from the web and used Veo and Imagen tools to automatically generate a short, travel‑themed cat video — illustrating multimodal workflows. In Google Cloud, the Vertex AI agent framework has been integrated with MCP so developers can host LLMs like Gemini and deploy MCP servers on Cloud Run and similar infrastructure, making MCP the standard agent integration pattern in Google’s ecosystem.
• Other ecosystems and open source: Salesforce plans to adopt MCP in its Agentforce platform, calling MCP the de facto standard for how agents access tools and data. Microsoft’s GitHub Copilot X is reportedly preparing MCP support in IDEs to interact with user codebases and issue trackers. IDEs like VS Code are experimenting with MCP clients to connect to file systems or tools like Sentry. Many companies — Atlassian (Jira), Cloudflare, MongoDB, PayPal — have released or piloted MCP connectors for their services. The developer community is vibrant: as of 2025, there are ~10 official SDKs, 1000+ MCP server implementations, and 80+ compatible clients, forming a robust MCP ecosystem. Public catalogs (e.g., mcpservers.org) list available MCP servers so teams can quickly discover and attach the right servers to their LLMs.

3. Standardization Trends and Industry Collaboration

From the outset, MCP has been developed as an open‑source standard, inviting broad industry participation and collaboration. Anthropic authored the initial draft and open‑sourced the spec and SDKs; with OpenAI and Google adopting it, MCP has become a de facto standard. Frequently described as “the common language of the AI industry,” MCP is viewed as the leading interoperability specification for the agent ecosystem.

OpenAI’s official support in ChatGPT is widely seen as a key milestone cementing MCP as the industry standard. Google has likewise integrated MCP into its model SDKs and agent tooling and has emphasized it to developers as “the new standard.” In this way, major AI research organizations have joined forces, making MCP a vendor‑neutral, open protocol rather than a single company’s property.

While there is no formal recognition from a traditional standards body, standardization is effectively being driven by the open‑source community. Anthropic and partners co‑develop and maintain MCP specifications and SDKs on GitHub, and numerous companies contribute reference MCP servers, expanding the ecosystem. For example, connectors from Cloudflare, Redis, and Atlassian are open‑sourced so others can reuse or enhance them. A public MCP server registry (e.g., mcpservers.org) has emerged, allowing teams to search hundreds of tools described in a standard format. Because MCP is a more open and consistent protocol than bespoke APIs, a connector built once can be reused across multiple agents (ChatGPT, Claude, Gemini), further accelerating standard adoption.

Enterprise security and governance requirements are another major driver. MCP supports fine‑grained permissions and authentication from its early design, meeting corporate needs. Administrators can control which toolsets are exposed via MCP and impose policies over the tools and data an agent can access. MCP servers can be connected securely via standard mechanisms like OAuth, and organizations can implement logging and monitoring of all tool executions. These security/control features make MCP easier to adopt in heavily regulated industries such as finance and healthcare, and in turn promote standardization.

In summary, MCP is an open standard consolidated through industry self‑coordination. With leadership from Anthropic, OpenAI, and Google — and contributions from many IT companies — the protocol has become the ecosystem standard. This community‑driven governance model is likely to continue, guiding the maintenance and evolution of MCP.

4. Future Opportunities and Potential

MCP is already transforming how LLMs perform information retrieval and tool use, and it has the potential to expand into much broader application areas — especially for multimodal AI agents, complex tool‑chained workflows, and on‑demand context switching:

• Expansion to multimodal agents: MCP can connect not only text‑based LLMs but also image, speech, and video models. For example, an agent might use MCP to call an image recognition API to identify objects in a photo, then invoke a text‑to‑speech tool to produce a spoken response — creating an agent that integrates visual and auditory information. Anthropic has suggested scenarios like an AI reading Figma designs to build a web app, or using Blender to generate a 3D design and sending it to a 3D printer, hinting at MCP’s multimodal potential. This paves the way for digital assistants that can use not only documents but also images attached to emails, calendar entries, and map data — combining multiple modalities fluidly.
• Complex tool chains and workflow automation: MCP will greatly facilitate agents that automate multi‑step tasks. As seen with ChatGPT and Gemini, a single user request can lead the agent to invoke multiple MCP connectors in sequence and chain their results to complete sophisticated jobs. Going forward, such tool chaining will become commonplace. For instance, given the abstract instruction “Process the customer order,” an agent could, via MCP, query inventory, approve payment, update the shipping system, and notify the customer — integrating multiple systems end‑to‑end. Because MCP standardizes the communication, systems for inventory, payments, logistics, and messaging can all speak the same protocol to the agent, which can compose tools dynamically to deliver end‑to‑end automation. This agentic orchestration will be particularly valuable in complex enterprise domains — IT operations, customer support, project management — and, thanks to MCP, can elevate RPA/workflow engines into natural language‑driven intelligent automation.
• On‑demand context switching and long‑term memory: MCP’s context management can unlock more flexible, “smarter” AI assistants. Because prompt windows are limited, an LLM can only use a bounded context at once; with MCP, however, the agent can fetch relevant information just‑in‑time and release it afterward, realizing on‑demand context. A personal assistant might connect to another MCP server on the fly to fetch relevant data and then disconnect, leaving unnecessary information out of the model’s context. MCP includes mechanisms for tool discovery, dynamic connect/disconnect, and state handling, making it practical for an agent to switch context sources or use multiple contexts in parallel. This also supports long‑term memory: by maintaining conversation history or a user database behind MCP servers and retrieving only what’s needed, agents can handle very long‑horizon tasks and dialogues without forgetting.
• Agent‑to‑agent collaboration and ecosystem growth: MCP enables not only agent‑tool interactions but also agent‑agent collaboration. Different specialized AIs can expose their capabilities via MCP servers and connect to each other as clients, collaborating much like human experts via APIs. For example, a coding‑specialist agent could produce a function that a math‑specialist agent verifies or optimizes — all via MCP exchanges. Because the call protocol is standardized, different models can request/respond in a shared “language,” fostering modularity across the agent ecosystem. Salesforce’s plan to bring MCP into Agentforce so that multiple enterprise agents share common toolsets illustrates this trajectory. Over time, we can expect app‑store‑like marketplaces of MCP‑compliant tools and agents, enabling ubiquitous AI services composed on demand.
• Opportunities in security and reliability: MCP’s spread also creates opportunities for safer tool use. The protocol contemplates authentication, authorization, approvals, and monitoring, opening the door to standard security modules and auditing tools. Enterprises might deploy an MCP gateway to whitelist approved servers and monitor for data exfiltration or misuse in real time. The industry could introduce standardized tool skill levels and trust marks certifying reliable MCP tools, guiding agents to prefer trustworthy tools and reducing incidents from misuse. In short, MCP helps build a secure, standardized ecosystem for tool use, accelerating social and enterprise adoption of AI.

Finally, MCP pairs naturally with RAG (Retrieval‑Augmented Generation). While RAG emphasizes retrieval and MCP emphasizes action, both aim to augment LLM intelligence. Within one framework, an agent can use an MCP server to query a vector DB (RAG‑like retrieval) and then invoke another MCP tool to act on the result. Through this unified use, future LLM agents will not only be more accurate and useful but also become complete task performers that affect the real world.

Summing up, the Model Context Protocol is emerging as a core infrastructure of the LLM era. Thanks to this open, standardized protocol, AI models can freely acquire needed context and safely execute external actions, while the industry explores shared progress around it. MCP is poised to underpin innovations such as multimodal intelligent agents, automated AI collaboration, and context intelligence — solidifying its role as “AI’s USB‑C.”