What is Model Context Protocol (MCP)?
Turkish: MCP (Model Context Protocol)
Model Context Protocol lets AI applications connect to tools, files, and external systems through a shared context interface.
What is Model Context Protocol (MCP)?
Model Context Protocol provides a shared connection layer between AI applications and external resources. The goal is to expose tools, resources, and reusable prompts in a standard format instead of building every integration from scratch.
An MCP setup usually has a client and one or more MCP servers. The client might be a desktop AI application or an internal assistant. A server can expose a file system, CRM, database, Git repository, or custom API to the model in a controlled way.
How It Works
An MCP server advertises the tools it can provide. When the AI application wants to use one, it sends a call with parameters, the server performs the operation, and the result is returned to the model. This makes tool access more predictable for agent-based systems.
MCP is not a complete security layer by itself. Authorization, secret handling, logging, and user approval still need to be designed separately. Permissions for file access, data writes, or external actions should be narrow and explicit.
Business Use
MCP can help AI agent applications reach company data, operations tools, and developer systems without a separate custom connector for every source. Each connection still needs risk review, unnecessary tools should be disabled, and outputs should be inspected before sensitive actions are taken.
Related Terms
Agentic AI is an AI approach where systems plan tasks, use tools, and adjust their next steps instead of producing a single reply.
AI AgentAn AI agent is a software component that uses an LLM, tools, and data sources to plan steps and complete a defined goal.
API (Application Programming Interface)An API is a contract that lets software systems request approved data or actions from one another through documented endpoints.
LLM (Large Language Model)An LLM is a model trained on large text datasets that can understand and generate natural language, forming the basis of tools like ChatGPT.