The Model Context Protocol (MCP) enables host applications like Claude Desktop to share structured context with AI models, ensuring the model can access relevant data while maintaining user control. MCP acts as a standardized interface that defines how applications transmit information such as user preferences, real-time app states, or historical interactions. For example, if a developer integrates Claude into a project management tool, MCP would allow the app to send task lists, deadlines, and user roles to Claude. The model then uses this context to generate responses tailored to the project’s specifics, like suggesting priority adjustments or flagging timeline conflicts. This avoids forcing users to manually retype details and keeps interactions focused on the app’s current state.
Technically, MCP operates through structured data formats like JSON or protocol buffers, with explicit schemas defining valid context types. Host apps send context packets alongside user queries, often via API calls or SDKs provided by the model’s platform. For instance, a code editor using Claude for debugging might use MCP to transmit the current file’s code, error logs, and dependencies. Security is handled through authentication tokens and permissions: apps must request access to specific context types (e.g., calendar events, documents), and users grant explicit consent. MCP also includes versioning to ensure backward compatibility as schemas evolve. Developers implement hooks in their apps to capture relevant context—like monitoring file changes in a text editor—and pass it to the model via MCP’s defined endpoints.
Practical use cases highlight MCP’s flexibility. In a customer support app, MCP could share ticket history and user account details with Claude, enabling personalized responses without exposing sensitive data. A healthcare app might use MCP to provide patient records (with proper anonymization) to generate summaries. Developers must consider tradeoffs: while MCP reduces manual input, overloading the model with irrelevant context can degrade performance. Tools like context window limits and priority tagging help manage this. For example, a design tool integrating Claude might prioritize sending the active artboard’s layers via MCP while excluding unused assets. Debugging typically involves validating schema adherence and monitoring context ingestion logs to ensure the model receives accurate, timely data.