YT – MCP vs API: Simplifying AI Agent Integration with External Data – IBM Technology
– LLM needs to communicate with external data sources to be truly useful
– Anthropic came up with the Model Context Protocol – “standardizes how applications provide context to LLMs”
– MCP is like a USB for your AI applications. USB can be used regardless of manufacturers.
– Each MCP Host Client opens a JSON RPC 2.0 session using the MCP protocol, and that connects to MCP servers (e.g. database, email storage)
– MCP does … 1) provides a standardized way to retrieve contextual data to AI Agents 2) enables usage of tools(web search, API call, calculation, db lookup) by AI Agents
– MCP server primitives … 1) tools: discrete actions or functions the AI can call 2) resources: read-only data items the server can provide e.g. db schema, file content 3) prompt templates
– AI agents can “query an MCP server to discover what primitives are available and invoke those capabilities in a uniform way”
– RESTful API communicates of HTTP using methods such as get, post, put, delete
– MCP supports dynamic self discovery – client can ask “what can you do?”
– MCP servers is also a wrapper around an API. Many of the tools are APIs. In this case MCP is an AI friendly interface of APIs.
– MCP systems are deployed in file systems, Docker, Spotify, Google Maps etc.