What is MCP (Model Context Protocol) and why does it matter?

MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems. Rather than requiring custom code for each new integration, AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts)—enabling them to access key information and perform tasks. Think of it as a universal adapter that solves a fundamental problem in AI deployment: how to give language models access to real-world data and capabilities without building custom integrations for every new tool.

Key Points

- MCP works like a USB-C port for AI applications—just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.

- Introduced by Anthropic in November 2024, MCP provides a secure and standardized "language" for LLMs to communicate with external data, applications, and services.

- MCP addresses the "N×M integration problem"—without a standardized protocol, each AI application must integrate directly with every external service, creating N×M separate integrations. MCP solves this by requiring each client and each MCP server to implement the protocol just once, reducing total integrations from N×M to N+M.

- In March 2025, OpenAI officially adopted the MCP, after having integrated the standard across its products, including the ChatGPT desktop app.

- In December 2025, Anthropic donated the MCP to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, co-founded by Anthropic, Block and OpenAI, with support from other companies.

Understanding the Model Context Protocol

As AI assistants gain mainstream adoption, the industry has invested heavily in model capabilities, achieving rapid advances in reasoning and quality. Yet even the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems. Before MCP, connecting an AI to a specific tool meant building a custom integration every time. This fragmentation created maintenance nightmares and made it difficult to build comprehensive AI systems that could work across multiple data sources.

MCP acts as a bridge, allowing AI to move beyond static knowledge and become a dynamic agent that can retrieve current information and take action, making it more accurate, useful, and automated. The protocol is designed to be model-agnostic, meaning MCP can give you the flexibility to switch between different LLM providers without losing access to your data sources, since the protocol is model-agnostic.

MCP re-uses the message-flow ideas of the Language Server Protocol (LSP) and is transported over JSON-RPC 2.0. This technical foundation allows for reliable, standardized communication between AI applications and external services.

How It Works

MCP operates through a three-part architecture:

1. The Host: The LLM is contained within the MCP host, an AI application or environment such as an AI-powered IDE or conversational AI. This is typically the user's interaction point, where the MCP host uses the LLM to process requests that may require external data or tools.

2. The Client: The MCP client, located within the MCP host, helps the LLM and MCP server communicate with each other. It translates the LLM's requests for the MCP and converts the MCP's replies for the LLM. It also finds and uses available MCP servers.

3. The Server: The MCP server is the external service that provides context, data, or capabilities to the LLM. It helps LLMs by connecting to external systems like databases and web services, translating their responses into a format the LLM can understand which helps developers provide diverse functionalities.

Model Context Protocol servers expose data through: Resources: Information retrieval from internal or external databases. Resources return data but do not execute actionable computations. Tools: Information exchange with tools that can perform a side effect such as a calculation or fetch data through an API request. Prompts: Reusable templates and workflows for LLM-server communication.

Why It Matters

By enabling AI systems to access real-time data beyond their LLM's training data, MCP helps AI models provide accurate, up-to-date responses rather than relying solely on static training data from their initial learning phase. This addresses a critical limitation of language models: they can only work with information they were trained on, which becomes outdated quickly.

LLMs, by nature, can sometimes make up facts or produce plausible but ultimately incorrect information (hallucinate) because they predict answers based on training data, not real-time information. The MCP helps reduce this by providing a clear way for LLMs to access external, reliable data sources, making their responses more truthful. For energy professionals, this means AI tools can access current grid data, real-time pricing information, and operational databases to provide accurate analysis and recommendations.

The Model Context Protocol MCP explicitly enables agentic workflows that rely on dynamic tool discovery and action primitives to perceive, decide, and act across systems. This makes it possible to build AI agents that operate autonomously while maintaining proper governance controls. In practice, this means organizations can deploy AI agents that can query databases, execute workflows, and take actions—all while maintaining security and audit trails.

Real-World Applications in Energy

MCP is already being applied to energy-specific challenges. EnergyPlus-MCP is the first open-source Model Context Protocol server specifically designed for EnergyPlus simulation workflows. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces.

An agent might normally optimize for throughput, but if energy prices spike (detected via an MCP service), it could switch to an energy-saving goal. Through MCP, all the relevant information (energy price feed, device control interfaces) are at the agent's fingertips, enabling such adaptive strategies to be implemented in a straightforward manner.

Related Terms

• LLM (Large Language Model): An AI model trained on vast amounts of text data that can understand and generate human language.
• API (Application Programming Interface): A set of rules that allows different software applications to communicate with each other.
• JSON-RPC: A lightweight protocol for remote procedure calls that uses JSON for data encoding.
• Agentic AI: AI systems that can autonomously perceive their environment, make decisions, and take actions to achieve goals.

Frequently Asked Questions

How is MCP different from function calling?

MCP connects AI apps to context while building on top of function calling—the primary method for calling APIs from LLMs—to make development simpler and more consistent. Function calling, which allows LLMs to invoke predetermined functions based on user requests, is a well-established feature of modern AI models. Sometimes referred to as "tool use," function calling is not mutually exclusive with MCP; the new protocol simply standardizes how this API feature works, adding context for the LLM.

Is MCP secure?

MCP prioritizes privacy by default. This means it requires explicit user approval for every tool or resource access. Servers run locally unless explicitly permitted for remote use, so sensitive data won't leave controlled environments without consent. However, security researchers released an analysis that concluded there are multiple outstanding security issues with MCP, including prompt injection, tool permissions that allow for combining tools to exfiltrate data, and lookalike tools that can silently replace trusted ones.

Can I use MCP with different AI models?

Yes. The beauty of MCP is that these same tools work identically whether accessed by Claude, GPT-4, or any other AI model — only the integration pattern differs. This vendor-agnostic approach is one of MCP's key advantages, allowing organizations to avoid lock-in to a single AI provider.

Last updated: May 24, 2026. For the latest energy news and analysis, visit stakeandpaper.com.