Understanding mCP: The Model Context Protocol Explained

mCP stands for Model Context Protocol, relevant for various AI models, especially large language models.

mCP significantly enhances the capability of AI models by providing necessary context for querying databases and reading files.

mCP servers provide four types of context primitives: tools, resources, sampling, and parameterized prompts.

Tools enable models to perform various actions, while resources are attachments like files needed for tasks.

Sampling allows AI models to query other models, whereas parameterized prompts are templates for requests.

mCP servers handle requests from clients, implementing tools and providing resources needed by the AI model.

Clients, like a chat interface, make requests to servers for tools and resources to accomplish tasks.

mCP employs a well-defined protocol for message structure that enhances interaction efficiency between clients and servers.

mCP supports two main transport mechanisms: standard IO for local communication and server-sent events for remote communication.

The choice of transport mechanism depends on the specific use case, with local access commonly using standard IO.

Developing an mCP server requires downloading source code and configuring client settings, which may include editing JSON files.

The mCP protocol's reflection feature allows clients to query servers for information on available tools and resources.

mCP offers tools for integrating large language models more effectively than general APIs like GraphQL or gRPC.

Unlike GraphQL, mCP provides a direct remote procedure call mechanism for tools, streamlining integration processes.

Use mCP as a complement to backend APIs for improved AI model interfacing, rather than as a full replacement.

Keep mCP servers focused on specific tasks, allowing clients to interface with multiple mCP servers for diverse functionalities.

Maintain high abstraction levels in mCP servers to provide AI models with necessary context without overwhelming them with low-level details.