AI Context Use Case - Naftiko Blog

This is blog post on the AI Context use case, focusing on providing Model Context Protocol (MCP) servers on top of common private, public/1st party and 3rd party APIs, as well as local SQL databases, employing a domain-driven, declarative, and governed approach to right-sizing the context windows via MCP while providing integrations for us across AI copilots and agents.

Title

  • AI Context

Tagline

  • AI without context is guesswork. Valuable data lives in SaaS tools, files, and systems your models can’t reach safely.

Description

This use case focuses on providing Model Context Protocol (MCP) servers on top of common private, public/1st party and 3rd party APIs, as well as local SQL databases, employing a domain-driven, declarative, and governed approach to right-sizing the context windows via MCP while providing integrations for us across AI copilots and agents.

Teams need a reliable way to deliver MCP servers from internal and third-party APIs without having to discover and learn about each API and the technical details of integration. This use case provides the fundamentals for safely integrating existing data and systems into artificial intelligence copilots and agents.

Benefits

  • Add data and tools to agents
  • Compose MCP servers
  • Aggregate & curate context

Pain

  • Copilot Leadership Mandate
  • MCP Leadership Mandate
  • Unmanaged Encryption
  • Unmanaged Discovery
  • Unmanaged Authentication
  • Unmanaged Usage
  • Unmanaged Cost

Gains

  • 3rd-Pary Data in Copilot
  • 3rd-Party MCP Available
  • Manage Budget Across
  • Managed Risk Involved
  • Optimize SaaS Usage
  • Create More Visibility
  • Create More Discovery
  • Create More Reusability

Connects

  • Internal APIs
  • Infrastructure APIs
  • SaaS APIs
  • Partner APIs

Adapters

  • HTTP
  • MCP
  • OpenAPI

Last modified January 5, 2026: update links (d1fcdb4)