Greeting

Generates contextually-aware, personalized greetings by exposing greeting logic as MCP tools that Claude and other LLM clients can invoke. The server implements the Model Context Protocol to register greeting generation as a callable resource, allowing LLM applications to dynamically request greetings with custom parameters (user name, context, tone) and receive structured responses that can be embedded directly into application flows without additional API abstraction layers.

Accepts structured input parameters (user name, interaction context, greeting tone/style, language preference) and generates greetings tailored to those specifications. The server likely implements parameter validation and conditional greeting logic that routes requests to different greeting templates or generation strategies based on input combinations, enabling applications to request greetings that match specific user engagement goals without hardcoding greeting strings.

Implements the Model Context Protocol server specification, including tool schema definition, request routing, and response serialization. The server registers greeting generation as a discoverable MCP tool with a defined schema (input parameters, output format, description), allowing MCP clients to introspect available capabilities and invoke them with type-safe parameter passing. Handles MCP protocol handshake, error responses, and graceful shutdown.

Enables applications to request greetings at contextually appropriate moments in user interactions (first visit, session start, after inactivity, milestone events) by accepting timing/context metadata as input parameters. The server can generate greetings optimized for specific interaction stages, allowing LLM applications to improve user engagement by delivering personalized messages at moments when users are most receptive, without requiring the application to maintain greeting scheduling logic.

Maintains a registry of greeting variants (different phrasings, tones, lengths) and selects the most appropriate variant based on context parameters, user attributes, or randomization strategies. The system likely supports weighted variant selection (e.g., 70% formal, 30% casual) and may use context signals to determine which variant maximizes engagement or user satisfaction.