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MCP (Model Context Protocol) is an open-source standard that gives AI applications a shared way to connect to external data, tools, and workflows. An AI application communicates through an MCP client with MCP servers that offer capabilities such as tools, resources, and prompts. MCP standardizes the connection; it does not provide every service or guarantee that an AI action is safe or correct.
Why does MCP exist?
An AI application may need information from another system or need to ask that system to do something. Without a shared interface, developers may have to build and maintain a separate integration for each application and service. MCP provides a common protocol for those connections, with the aim of making it easier for AI applications and external systems to interoperate. The official introduction compares this role to USB-C: a useful analogy for a shared interface, not a claim that MCP is a physical connector. Model Context Protocol’s introduction
How does MCP work?
MCP uses a client-server architecture. The host is the AI application. Inside it, an MCP client communicates with an MCP server, which exposes capabilities from an external system. The protocol’s data layer uses JSON-RPC-based messages, while its transport layer carries those messages between client and server. The architecture overview
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- Client: The protocol participant in the host that communicates with a server.
- Server: The component that makes capabilities available to the application.
These are distinct roles, even when the details are hidden behind an application’s interface. MCP defines how the participants communicate; the connected service and the host determine what is actually available to the user.
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What are MCP tools, resources, and prompts?
Servers can expose three different kinds of capability. They are not interchangeable: tools perform actions, resources provide information, and prompts offer reusable interaction templates. The MCP tools specification
- Tools: Actions a model can request, such as querying a database or calling an API. A server describes its tools and their input schemas; a client can list and call them.
- Resources: Contextual data made available to the AI application, such as a database schema.
- Prompts: Reusable interaction templates, which may include examples.
For a database integration, a server might offer a query tool to retrieve results, a schema resource to explain the available tables, and a prompt containing an example query workflow. That illustrates how MCP can make both information and actions available through one protocol without making them the same capability.
What MCP does not guarantee
MCP support is not a blanket safety guarantee. A tool may affect an external system, and the model can request that the tool be invoked. The specification says that, for trust, safety, and security, there “SHOULD always be a human in the loop with the ability to deny tool invocations.” It also recommends making tool activity visible. This is guidance in the specification, not a promise that every MCP client uses the same approval screen or controls. Users should be able to see which tools are available and retain meaningful oversight of consequential calls. MCP tools specification
The Tool Desk
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The latest release covered here is the MCP specification dated July 28, 2026. Its announcement highlights a stateless protocol core, multi-round-trip requests, header-based routing, cacheable list results, authorization hardening, a formal extensions framework, and updates to Tier 1 SDKs. These are version-specific implementation changes, not prerequisites for understanding the basic host-client-server model. MCP’s July 28, 2026 release announcement
The release retires the initialize/initialized exchange and the Mcp-Session-Id header in the new version. It also deprecates the legacy HTTP+SSE transport with a year-long offramp, and deprecates Roots, Sampling, and Logging while stating they will continue working for at least twelve months. Setup instructions written for older versions may therefore differ; check that a tutorial identifies the specification version it targets before applying its steps.
How should you think about MCP?
MCP is a shared software interface between AI applications and external systems. Its value is that applications and services can communicate through a common protocol rather than relying only on bespoke wiring. It does not supply the connected data or services, decide whether a requested action is appropriate, or ensure that a model’s result is correct. Those depend on the server, the host, and the controls around their use.
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