Give AI clients a useful connection
Model Context Protocol provides a common interface for AI clients to discover and use tools and resources. An MCP integration can expose approved business context without requiring your team to rebuild the same integration for each compatible client.
What I engineer
- MCP servers exposing bounded tools and resources.
- Streamable HTTP integrations where appropriate.
- Authentication, connection permissions, and tenant isolation.
- Source retrieval and contextual search.
- Reviewed learning or suggestion workflows instead of unrestricted writes.
A working reference
For Narravo, the implemented MCP endpoint exposes company context, search, and source retrieval. Learning submissions are restricted to connections that allow suggestions and enter a review workflow. The implementation was exercised through an MCP client; that engineering evidence does not imply every possible client or production environment has been certified.
Common questions
Is MCP the same thing as RAG?
No. MCP describes an interface for tools and resources. Retrieval may be one capability behind that interface.
Should an AI client be allowed to update company knowledge?
Only through a deliberate permission and review model. Suggestions should not silently become authoritative facts.
Can the integration work with different models?
Compatibility depends on the AI client and supported transport, rather than the model name alone. See company knowledge through MCP.