A concise explainer of Model Context Protocol (MCP): the standard connector that lets LLMs securely access calendars, drives, email, and other tools so AI can take real-world actions.
What is MCP?
MCP, or Model Context Protocol, is an open-style specification that defines a single, consistent way for language models to call out to external tools and services. Instead of building a custom connector for every app (Google Drive, Slack, calendar, etc.), MCP provides a universal “connector” so AI can request actions like saving files, creating events, or sending emails.
How AI chat works today — and its limits
Current AI chat interactions are great for questions and explanations because the model relies on its own knowledge. But when you want the AI to perform an action—save a file, schedule a meeting, or fetch private data—the model needs a way to securely talk to your applications. Without a standard, each integration becomes a separate plugin or connector.
How MCP solves the connector problem
Think of MCP like USB-C for AI tools: one standard interface that works across many services. An MCP Server acts as the bridge between the LLM and your apps, handling authentication, error handling, and service-specific details so the AI only needs to speak one language.
Example flow — Save conversation to Google Drive
- User: “Save this chat as conversation.txt on my Google Drive.”
- LLM: Sends a standardized MCP request to the MCP Server.
- MCP Server: Handles Drive authentication, uploads the file, and returns success or detailed error information.
Key benefits of MCP
- Simplicity — One connector for multiple tools; reduce custom plugin sprawl.
- Speed — Add or swap services quickly without weeks of custom code.
- Power — Enable chained tasks: fetch, analyze, and store in a single workflow.
- Security — Centralized controls and standard checks so the AI only performs permitted actions.
How to get started
If you’re evaluating AI platforms or building your own assistant, look for MCP support. You can run or deploy an MCP Server for the tools your organization uses most, and then provide scoped permissions so the LLM can act safely on behalf of users.
Key takeaways for Salesforce teams
- Admins and integrators can reuse a single MCP implementation to connect Salesforce, document stores, and messaging apps.
- Developers can build fewer bespoke connectors—focus on secure mapping and governance instead.
- Business users get more reliable, actionable AI: scheduling, record updates, and exports that behave predictably.
For Salesforce admins, developers, and business users, MCP is an important step toward turning AI from a conversational assistant into a capable, secure, and auditable automation layer that integrates with the systems you already use every day.