@transcend-io/mcp-server-preferences

Exposes preference management capabilities through the Model Context Protocol (MCP) standard, allowing Claude and other MCP-compatible clients to discover and invoke preference operations via a standardized tool interface. Implements MCP server specification with JSON-RPC 2.0 transport, enabling seamless integration into LLM agent architectures without custom protocol negotiation.

Provides Create, Read, Update, Delete operations on user preferences through MCP tool definitions that Claude and other LLM clients can invoke. Each operation is exposed as a discrete tool with input validation, error handling, and structured response formatting, enabling LLMs to manipulate preference state as part of multi-step agent workflows.

Enables MCP clients to retrieve and cache user preference context that can be injected into LLM prompts and decision-making. The server exposes preference data in a format optimized for LLM consumption, allowing agents to make context-aware decisions based on stored user settings without requiring separate API calls for each decision point.

Validates incoming preference data against a predefined schema before persistence, enforcing type constraints, required fields, and format rules. Uses JSON Schema or similar validation framework to ensure preference integrity at the MCP server boundary, preventing malformed data from reaching the backend store and reducing downstream validation burden.

Implements user-scoped preference access control at the MCP server level, ensuring that preference operations are automatically scoped to the requesting user's context. Uses user identifiers from the MCP client context to enforce isolation, preventing cross-user preference leakage and enabling safe multi-tenant preference management without explicit authorization checks in application code.

Emits events when preferences are modified, allowing MCP clients and downstream systems to react to preference changes in real-time. Implements an event-driven architecture where preference mutations trigger notifications that can be consumed by webhooks, message queues, or in-process listeners, enabling reactive preference synchronization across distributed systems.

Maintains immutable history of all preference changes with timestamps and actor identity. Supports temporal queries to retrieve preference state at any point in time, enabling audit trails and compliance reporting. Implements efficient storage using event sourcing or change logs, with optional archival to cold storage for older records. Provides time-range queries, change-diff operations, and historical snapshots for compliance documentation.