When an organization uses several AI tools, its knowledge can become several disconnected copies. The problem is not only transporting documents. It is keeping context attributable, current, and governed as different clients use it.
Model Context Protocol provides an interface for exposing tools and resources to compatible AI clients. A useful implementation starts with deciding what those clients should be able to access.
Separate the interface from the knowledge system
MCP is not a knowledge database and is not interchangeable with RAG. It describes how a client discovers and interacts with capabilities. Your application still owns the knowledge structure, retrieval behavior, permissions, and review policy.
That separation makes the integration easier to reason about. A source retrieval tool can return evidence from an existing application without recreating the entire product inside the protocol layer.
Expose bounded capabilities
Narravo implements company context, search, and source retrieval through a company-scoped MCP endpoint.
Each capability has a recognizable purpose. Context gives a bounded packet of company knowledge. Search helps locate relevant material. Source retrieval lets a client inspect the evidence behind that context.
A broad tool that can read or mutate arbitrary application state would be harder to authorize and evaluate.
Keep authentication and authorization distinct
A valid connection does not establish access to every company or every document. The server must still apply the connection's company scope and permitted capabilities.
The Narravo implementation uses authenticated connections and permission checks. Its recorded engineering checks include read-only behavior, revocation, source isolation, and authentication failures. Those checks support the implementation story; they are not a claim of certification across every MCP client.
Credential-based internal connections and public integrations have different deployment considerations. Design against the supported client and the applicable protocol requirements rather than assuming one authentication pattern fits every context.
Let knowledge return as a suggestion
A client may learn something useful in a conversation. That does not mean it should overwrite an approved company fact.
Narravo's learning submissions require a connection that permits suggestions. Proposed knowledge enters a review inbox. An editor determines what becomes approved.
This is a general design principle: keep the ability to propose separate from the authority to publish.
Evaluate the connection end to end
Test initialization, capability discovery, valid calls, malformed input, expired or revoked access, and attempts to access another company's sources.
Check the deployed route too. Protocol tests do not prove a proxy, database connection, or production authentication configuration behaves correctly.
The portfolio's MCP simulation explains the context-and-review loop with fictional data. It does not connect to a visitor's model or execute a real MCP session.
If you need company knowledge available across compatible AI tools, explore MCP integration development.