Model Context Protocol standardizes how an AI application connects to tools and context providers. It is not a shared database between models, an automatic encryption layer or a guarantee that an assistant's actions are safe. The host application still decides how to use context and enforce business controls.
What does the protocol provide?
MCP architecture describes hosts, clients and servers and a JSON-RPC data layer. A host manages its clients; a server exposes supported capabilities. Use the specification and SDK documentation for the version you deploy, rather than assuming a class or method exists from its name.
| Capability | Illustrative purpose | Application responsibility |
|---|---|---|
| Resource | Read an approved policy document | Apply document access and version rules |
| Tool | Retrieve an order or propose a change | Validate arguments and check permission |
| Prompt | Offer a reusable interaction template | Treat the template as guidance, not authorization |
Design one narrow tool first
In a hypothetical support workflow, begin with an order lookup that returns only fields the caller may see. Give it a precise input schema and a clear not-found response. A write tool should be separate, with explicit permitted transitions and approval rules. Reading an order must not implicitly grant permission to cancel it.
The protocol connection does not replace server-side checks. OWASP authorization guidance recommends least privilege and permission validation on each request. A successful connection establishes communication, not the caller's entitlement to every record.
Keep retrieved content outside the control boundary
An order note or document might ask the model to ignore rules. It remains untrusted business data. Limit tool permissions, validate outputs and record actual action results. Do not describe prompt filtering as complete protection from injection.
What should the integration test cover?
Capability discovery for the selected protocol version, valid and invalid arguments, unauthorized records, expired access and destination failures. Use the real SDK's examples with pinned dependencies when implementing code. Verify every imported class and called method against that SDK version. For AI integration, scope the tools and permission boundaries before choosing the deployment topology.
Choose the workflow before the AI integration interface
List the tools an assistant needs, the data each tool may read, and the actions that require approval. A narrow pilot can establish whether the business needs document retrieval, a conversational assistant, or a workflow spanning several systems. MCP is an interface choice; it does not establish permission to act on its own.