What it is
MCP is an open protocol created by Anthropic that gives AI models a standardized way to connect to external systems — databases, files, APIs, and business tools. Instead of building a one-off integration for every AI and every data source, MCP acts as a shared language both sides already speak. That means an AI agent can pull live data, trigger actions, and work with your actual business context rather than operating in isolation.
Why it matters
AI models are smart but blind - they can't see your files, databases, or tools unless you connect them. MCP standardizes how that connection works, so you build once and it works across different AI systems.
Key components
- MCP servers
- MCP clients
- Standardized tool calls
- Live data access
- Cross-platform compatibility
MCP vs A2A
Connects AI to tools and data
Connects AI agents to each other
How it connects
Salesforce's Agentforce platform supports MCP, meaning your AI agents can use it to securely access Salesforce data, external APIs, and tools in a standardized way — reducing the custom plumbing typically required to get agents working with real business data.
Good to know
MCP is still maturing, and not every tool or platform has built MCP support yet — check whether your key systems have an MCP server available before assuming out-of-the-box compatibility.
Related terms
A2A (Agent-to-Agent Protocol)
Google's open protocol that allows AI agents from different vendors to communicate and collaborate with each other.
AI Agent
An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specific goals - without constant human direction.
RAG (Retrieval-Augmented Generation)
A technique that makes AI smarter by fetching relevant information from your data before generating a response. The AI "looks it up" instead of guessing.
MCP (Model Context Protocol) in practice
Articles from the Funnelists Team that put this term to work.
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