Model Context Protocol
MCP is an open standard that defines how LLMs integrate with external tools, data, and context.
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Model Context Protocol
Model Context Protocol (MCP) is an open standard for giving language models structured access to tools, data, and prewritten prompts. Instead of relying on ad-hoc prompt engineering or custom retrieval pipelines, tools and data sources are exposed to models through MCP servers in a standardized format. Developers can plug in existing tools (both cloud e.g., GitHub, Slack and local e.g., company database) rather than reinvent integrations each time. This approach promotes reusability across models and projects. An MCP client (usually inside an AI app or agent—an MCP Host) is used to query these servers to retrieve information or invoke actions.
For MCP to work, the model (or its agent wrapper) must support tool use. MCP doesn’t overcome model limits like context window size or reasoning errors; it merely provides a standard for managing model context.
Further reading
Build an MCP Server and Build an MCP Client tutorials by ModelContextProtocol.io — The official MCP documentation is a great place to get started with MCP. The tutorials are easy to follow and can give you a solid grasp of how MCP Servers and Clients work.
Model Context Protocol servers — This is a very comprehensive list of existing MCP servers.
Everything Wrong with MCP by Shrivu Shankar — This article does a great job explaining the limitations of MCP.
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