An MCP server connects an AI application to an API or other data source through the Model Context Protocol (MCP). It presents capabilities—such as tools for actions or resources for information—in a standard format, then handles the integration work when the application requests them. The host application coordinates the model and connection; the MCP server neither replaces the underlying API nor decides how the model uses the results.
Where the MCP server fits
In MCP architecture, the host is the AI application. It creates an MCP client to connect to a particular server. A host can manage multiple clients, while each client connects to one server. The server implements the protocol-facing interface for an integration.
For example, a server might offer a tool that lets an AI application look up a customer record through an existing service API. The MCP server handles the protocol request and the integration-side call; the service API remains the system that owns the data and its business rules. An MCP server is therefore not necessarily the API server itself.
The protocol standardizes how the application and server exchange capabilities and results. As the Model Context Protocol Architecture overview puts it, “MCP focuses solely on the protocol for context exchange—it does not dictate how AI applications use LLMs or manage the provided context.”
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What happens during an API integration workflow
- The host connects. The AI application creates an MCP client and connects it to the server that fronts the API or data source.
- The client discovers capabilities. The client learns which protocol capabilities and primitives the server supports. The precise discovery sequence and version behavior depend on the protocol versions implemented by the host and server; consult their current documentation.
- The server makes selected functions available. Depending on its implementation, it can expose tools, resources, prompts, or a subset of them.
- The host requests an operation or information. When the host needs an action or context, its client sends the appropriate MCP request to the server.
- The server performs integration work and returns a result. It may call an underlying API, apply integration logic, and return a protocol result. The host can then decide how to use that result in the application.
The server does not automatically control the model’s reasoning or receive the full conversation. Those are application-level responsibilities, not behavior guaranteed by MCP.
What an MCP server can expose
MCP defines three useful kinds of server primitive. A particular server need not provide all three; check its documented capabilities rather than assuming they are present.
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| Primitive | Role in an integration | Example |
|---|---|---|
| Tools | Enable the host to request an action. | Call an API operation to create a ticket or search for an order. |
| Resources | Provide data the host can use as context. | Return a document or a record retrieved from a service. |
| Prompts | Provide reusable interaction templates. | Offer a consistent template for summarizing a retrieved record. |
These definitions describe the roles of the primitives, not a guarantee that every example or capability is implemented by a given server. See the protocol’s architecture documentation and concepts documentation for the protocol model.
What MCP does—and does not—standardize
- It standardizes the exchange between an MCP client and server. It gives the AI application a consistent way to discover and request the capabilities the server exposes.
- It does not replace the underlying API. The API, its credentials, business rules, and any resulting changes remain part of the integration.
- It does not prescribe model orchestration. The host application determines how to provide results to its model and what to do with the model’s response.
- It does not make every integration equivalent. Servers can expose different operations, data, permissions, and side effects.
Local and remote server connections
The architecture documentation describes stdio for direct communication with a local process and Streamable HTTP as a transport suitable for remote connections. The MCP protocol data format can be carried over supported transports, but the deployment and compatibility details differ.
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Before choosing a transport, verify that the target host supports it and check the current specification and implementation documentation. Authentication is also a deployment decision: the architecture material describes HTTP authentication options and recommends OAuth for obtaining authentication tokens. Do not assume that a host, server, or API supports a particular authentication flow without confirming it.
Google Cloud’s documentation describes remote MCP endpoints for using Google and Google Cloud services in AI applications with governance, security, and access controls. That is a vendor-specific example, not a requirement for MCP generally. The protocol’s architecture overview describes the roles and transports; its 2026-07-28 specification is a version reference, not a guarantee that every host or server implements that version.
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Security and integration design checks
An MCP server can make sensitive information accessible or offer tools that change data. Review the integration as a trust boundary, not just as a convenient protocol adapter. OpenAI’s remote MCP guidance highlights prompt-injection risks and the possibility that a server may request sensitive data a user would not want to share.
- Limit exposed operations. Make available only the API calls and data needed for the task.
- Separate read-only access from side effects. Identify tools that create, update, or delete data, trigger external actions, or otherwise have consequences.
- Scope credentials. Use credentials and authorization boundaries appropriate to the exposed capabilities rather than granting unnecessary access.
- Review server identity and definitions. Check who operates the server, what each tool claims to do, and how inputs and outputs are handled.
- Plan for sensitive context. Consider what data could be sent to the server and how returned content could influence the host or model.
These checks matter whether the MCP server calls a third-party API or serves as a gateway to an organization’s own systems. MCP provides the exchange mechanism; it does not by itself establish that a server or its requested permissions are trustworthy.
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How to compare two MCP integration designs
There is no single best design independent of the API, host, and deployment. Compare candidate integrations on the actual access and operational boundaries they create:
- Exposed data and operations: Which API functions and records are available through each server?
- Side effects: Which tools only read data, and which can change data or trigger actions?
- Credentials and authorization: What credentials does each design use, and how narrowly can access be scoped?
- Transport and host compatibility: Does the design use local stdio or remote HTTP, and does the intended host support that choice?
- Operations ownership: Who maintains the server, monitors its availability, and responds to failures?
These criteria help reveal practical differences; the protocol documentation does not rank particular implementations or declare one transport universally preferable.
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