What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open standard developed to streamline how AI applications, especially those powered by large language models (LLMs), connect with external tools, APIs, and data sources. MCP allows LLM-based agents to discover, access, and interact with resources through a unified, secure framework enabling more intelligent, context-aware, and scalable AI systems.
How MCP Works
Traditional integrations with APIs and tools have become inconsistent and vendor-specific. MCP solves this challenge by standardizing how AI applications interface with external services, making it easier to scale and secure AI workflows.
MCP has three core components:
- MCP Client: Embedded within the AI application to send structured requests.
- MCP Server: Exposes data, tools, and prompts the LLM can use.
- MCP Communication Layer: Uses secure transports (like JSON-RPC over HTTPS) to enable interaction between the client and server.
What is an MCP server?
MCP Server acts as a secure bridge between AI models and the outside world, enabling smarter, more contextual, and scalable AI workflows. At its core, an MCP Server exposes functionality and resources that an AI application (via an MCP Client) can call and use in real-time. These include:
- Tools: Executable functions such as APIs, databases, or scripts that AI agents can call to perform tasks.
- Resources: Files, documents, or media assets that provide essential context for LLMs.
- Prompts: Dynamic or static templates that guide the LLM in using a specific tool or process.
When an AI system queries the MCP Server, it receives a structured list of these elements. The server enables the LLM to interact intelligently with those capabilities using its function-calling mechanism.
