MCP is an open standard for connecting AI applications to external tools and data sources via “MCP servers”. It standardizes discovery and access to Tools, Resources, and Prompts, so clients (chat apps, IDEs, agent runtimes) can plug into servers consistently—often described as a “USB-C for AI”. [1][2]
MCP (Model Context Protocol) gives AI applications a standard way to talk to external tools, data, and prompts. With MCP, integrations become reusable “capability servers” that any MCP client can consume. Use the tabs above to jump to Core concepts, Transports, Architecture, How to use MCP, RAG + MCP, and Security.
MCP defines a client–server architecture: the AI application is the MCP client, and each integration is an MCP server that exposes a catalog of tools/resources/prompts. This makes capability discovery and invocation consistent across clients. [2][1]
Bonus: the MCP ecosystem also includes an MCP Registry concept (like an app store of servers) which helps discovery of publicly available servers. [8]
MCP supports different transport mechanisms depending on the environment and deployment style.
Rule of thumb: use stdio when the server is local to the user (IDE workflows, local file access), and use HTTP-style transports for shared enterprise servers (central auth, logging, scaling). [3][4]
For debugging and validating your server schemas and responses, the MCP Inspector is a common dev tool. [5][6]
MCP sits between the AI client and your systems, standardizing discovery and calls.
This client–server split is the key design point: clients decide what context to attach and when to call tools; servers own the integration logic and enforce boundaries. [2][1]
Start small: define a server that exposes a handful of tools and a few resources. Validate schemas and behavior with the MCP Inspector, then broaden capability surface gradually. [5][6]
If you want a catalog of reference servers and examples, the MCP docs provide an “Example Servers” list. [7]
MCP is a clean way to “package” RAG as a reusable capability: the client calls a retrieval tool, receives top documents/chunks, then composes the final answer.
A standard RAG loop can be expressed through MCP as: (1) client asks the MCP server to retrieve relevant context, (2) server queries a vector database (or search index), (3) server returns chunks + metadata, (4) client injects those chunks into the model prompt and generates the answer. This isolates retrieval logic from the AI client while keeping orchestration in the client. [1][2]
Concrete MCP pattern for RAG: expose a single tool (e.g., rag_search) that takes a query and returns a bounded list of passages with doc IDs/URLs/titles. Some RAG MCP implementations explicitly require a rag_search tool. [9]
If you want to implement quickly, there are community write-ups on building an MCP “RAG server” pattern (use as inspiration, then harden for security and governance). [10]
MCP is a clean way to “package” RAG as a reusable capability: the client calls a retrieval tool, receives top documents/chunks, then composes the final answer.
A standard RAG loop can be expressed through MCP as: (1) client asks the MCP server to retrieve relevant context, (2) server queries a vector database (or search index), (3) server returns chunks + metadata, (4) client injects those chunks into the model prompt and generates the answer. This isolates retrieval logic from the AI client while keeping orchestration in the client. [1][2]
Concrete MCP pattern for RAG: expose a single tool (e.g., rag_search) that takes a query and returns a bounded list of passages with doc IDs/URLs/titles. Some RAG MCP implementations explicitly require a rag_search tool. [9]
If you want to implement quickly, there are community write-ups on building an MCP “RAG server” pattern (use as inspiration, then harden for security and governance). [10]
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