Model Context Protocol (MCP) Concepts
MCP is a protocol pattern for connecting AI applications to external tools and resources through a standardized interface. A host application may connect to servers that expose tools, resources, or prompts. Standardization can simplify integration, but it does not automatically make a server trustworthy or secure.
Treat each MCP server as a dependency with its own permissions, credentials, network access, and data exposure. Review tools before enabling them, scope credentials, require approval for sensitive operations, and validate results. Distinguish protocol interoperability from authorization: the host still decides which user can invoke which tool.
MCP-style boundary
AI host
-> selects an approved MCP server
-> discovers available tools/resources
-> applies user and tool policy
-> invokes a scoped tool
-> validates result
-> returns relevant data to model💡 The protocol standardizes interaction patterns, but the host and server still need secure authorization, validation, and auditing.
Key Takeaways
- MCP standardizes integration patterns.
- Treat servers as external dependencies.
- Apply least privilege and audit sensitive tool use.
Knowledge Check
Does using a standardized tool protocol automatically make every tool safe?