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The Model Context Protocol (MCP) is an open protocol that lets AI applications connect to external tools and data through a shared interface. An AI application, or host, uses MCP clients to communicate with MCP servers; servers make capabilities such as tools, resources, and prompts available. MCP defines both the messages exchanged and the transport that carries them.
What is the Model Context Protocol?
MCP is a standard for connecting AI applications to systems where useful data and actions live. Rather than each AI application needing a different integration for every service, MCP gives clients and servers a common protocol for discovering and using capabilities. It does not provide those services itself, nor does it guarantee that a particular AI model can use every capability a server offers.
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The project describes MCP as a layered protocol: a data layer defines messages and capabilities, while a transport layer handles the connection and message delivery. See the MCP architecture guide.
How MCP works, step by step
- An AI application acts as the host. The host coordinates connections to one or more MCP servers.
- The host connects through an MCP client. The client communicates with a server using an available transport.
- Client and server establish compatibility. MCP includes lifecycle management and capability negotiation. Implementations must support the base protocol and lifecycle components; other components are optional according to the application’s needs, as described in the Basic Protocol Overview (2025-11-25).
- The server makes capabilities available. These can include tools, resources, and prompts. The host and its user can make use of capabilities supported by the client and application.
- They exchange protocol messages. The specification states: “All messages between MCP clients and servers MUST follow the JSON-RPC 2.0 specification.”
- A transport carries those messages. The transport determines how the connection is established and messages move between client and server.
What do host, client, and server mean?
| Term | Role in MCP |
|---|---|
| Host | The AI application that coordinates connections to MCP servers. |
| Client | The protocol component through which the host communicates with a server. |
| Server | A program or service that exposes capabilities or data to a client. |
These are distinct roles, even when an end user experiences them as one AI application. The host may connect to multiple servers, and each connection uses a client-server relationship.
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What can an MCP server provide?
Tools
Tools are callable operations a server exposes. A tool might let an AI application request an action from an external system, subject to the tool’s definition and the client’s behavior.
Resources
Resources are data or content that can supply context to an application. The server determines what it exposes; MCP does not mean that a client automatically has access to all data in the connected system.
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Prompts
Prompts are reusable interaction templates provided by a server. Together, tools, resources, and prompts are the main server capability types described in the architecture guide.
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How are MCP messages different from transports?
The data layer defines what clients and servers communicate: protocol messages, lifecycle behavior, and capabilities. The transport layer defines how those messages travel and how the connection is established. Both are part of MCP, but they solve different problems.
| Transport choice | What to understand |
|---|---|
| STDIO | A transport choice for communication involving standard input and output. Its process setup and connection behavior depend on the implementation. |
| HTTP | A transport choice that carries MCP communication over HTTP. Implementations must follow the relevant specification revision and their deployment’s authorization setup. |
STDIO and HTTP are not interchangeable names for the same mechanism. When evaluating an integration, check its documentation for where the server process runs, how the connection is established, and which transport it uses.
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What should you know about authorization?
The 2025-11-25 protocol overview specifies an authorization framework for HTTP-based transports. It says implementations using HTTP should conform to that framework. STDIO implementations should not use the HTTP authorization framework; they should instead retrieve credentials from the environment. This is a rule tied to that specification revision and transport, not a claim that every MCP deployment handles authentication identically. Check the implementation’s documentation and the protocol revision it supports before configuring credentials.
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The MCP project’s 2026-07-28 specification announcement lists a stateless protocol core, multi-round-trip requests, header-based routing, cacheable list results, authorization hardening, an extensions framework, and updated Tier 1 SDKs. These are changes associated with that release announcement; support can vary by client, server, and SDK. Check the revision and extensions implemented on both sides rather than assuming every integration has them.
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What to check when choosing or evaluating an MCP integration
- Transport: Is it STDIO or HTTP, and how does the implementation establish the connection?
- Capabilities: Which tools, resources, and prompts does the server actually expose?
- Authorization: How are credentials obtained, scoped, and handled for this transport and revision?
- Revision and extensions: Which protocol revision and optional extensions do the client and server support?
These checks help distinguish integrations that share the MCP label but expose different capabilities or use different connection and authorization arrangements. The protocol materials cited here do not establish a single performance or security ranking across implementations.
What MCP does—and does not—standardize
MCP standardizes a way for AI applications to communicate with external tools and data. It defines roles, capabilities, message conventions, and transport choices. The server still determines what it makes available, and the host, client, and model determine how those capabilities can be presented and used. MCP is therefore an integration protocol, not a bundled collection of services or a promise of a particular AI outcome.
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