Model Context Protocol (MCP)

Before MCP, connecting an AI assistant to enterprise systems meant building custom integrations for every pairing: one connector for the AI to query Salesforce, another for Jira, another for internal databases. Each integration was bespoke, fragile, and duplicated across teams. MCP standardizes this relationship by defining a client-server protocol: the AI model (client) discovers available capabilities, requests data or actions, and receives structured responses from MCP servers that wrap any underlying system.

The architecture has three components: the **MCP host** (the AI application — a chatbot, agent, or IDE), the **MCP client** (the protocol handler embedded in the host), and **MCP servers** (lightweight services that expose tools, resources, and prompts from a specific data source or API). An enterprise might run MCP servers for its CRM, code repository, internal knowledge base, and monitoring stack — all discoverable by any MCP-compatible AI application without custom code.

The business value compounds with scale. A single MCP server for your Confluence instance can be consumed by every AI tool your organization deploys — coding assistants, support agents, executive dashboards — without re-integration. Enterprises adopting MCP report 60–80% reductions in AI integration development time. Critically, because all context requests flow through defined MCP servers, every data access is auditable, permission-scoped, and governable.