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Model Context Protocol (MCP)
The Model Context Protocol is an emerging open standard designed to create a robust and reliable interface between AI agents, particularly those powered by Large Language Models, and external functionalities or tools"
Its primary goal is to empower AI agents to move beyond merely generating text and to actively interact with the real world, perform actions, and retrieve dynamic information that isn't hardcoded into their training data. MCP is a standardized, machine-readable way for an AI agent to understand and leverage external capabilities.
MCP defines a consistent format for describing what a tool does, what inputs it requires, and what kind of output it will return. This description is provided to the LLM within its context window. Unlike simply giving the LLM a natural language explanation, MCP uses a structured schema that the LLM is specifically trained or fine-tuned to interpret reliably.
When an LLM determines that a user's request requires the use of an external tool, MCP specifies how the LLM should format the request to execute that tool. This typically involves specifying the tool's name and the values for its required parameters. This structured tool call is then intercepted by an external orchestrator or agent runtime environment, which actually executes the tool.
Extreme caution should be taken when using MCP. MCP can expose you to command injection vulnerabilities, tool poisoning attacks, and many other security vulnerabilities. It has no built in authentication mechanism and no way to verify tool integrity.
Once the external tool has completed its operation, MCP dictates how the tool's output should be formatted and returned to the LLM. This output is then inserted back into the LLM's context window. The LLM, by processing this structured result alongside the ongoing conversation, can then interpret the outcome of the tool's execution and integrate that information into its subsequent responses to the user, potentially performing further steps or summarizing the findings.
Essentially, MCP functions as a common language or contract that allows various AI agents to seamlessly discover, understand, and interact with a diverse ecosystem of external services. By standardizing this interface, MCP aims to make it easier to build sophisticated AI agents that can plan multi-step operations, execute real-world actions, retrieve up-to-date information, and ultimately provide more dynamic and useful responses to users.