Memory Box MCP Server

Persists user memories with semantic embeddings and structured metadata formatting, enabling later retrieval by meaning rather than keyword matching. Implements a vector-backed storage layer that captures memory content, context, and relationships, allowing memories to be organized with custom schemas and formatting templates that preserve both raw content and semantic meaning for downstream search operations.

Retrieves memories by semantic similarity rather than exact keyword matching, using vector embeddings to find contextually relevant memories even when search queries use different phrasing or terminology. Implements approximate nearest-neighbor search over the memory embedding space, allowing developers to query memories by intent, topic, or concept rather than requiring exact recall of how the memory was originally phrased.

Applies structured formatting templates and schema validation to memories at save time, ensuring consistent organization and enabling structured queries. Implements a schema-based validation layer that enforces field presence, type correctness, and format compliance, allowing memories to be organized by custom categories, tags, and metadata fields defined by the user or application.

Exposes memory operations (save, search, format) as MCP tools that Cline can invoke directly within its agentic workflow, using the Model Context Protocol to standardize tool definitions, request/response schemas, and error handling. Implements MCP server endpoints that register memory tools with Cline's tool registry, allowing the AI assistant to autonomously decide when to save context, retrieve relevant memories, or format information without explicit user prompting.

Retrieves memories contextually relevant to the current task or conversation, using the agent's current state (file being edited, conversation history, task description) to filter and rank memory results. Implements context-aware retrieval by combining semantic similarity with task-specific metadata filtering, allowing the agent to surface the most relevant memories without explicit user queries.

Enables querying memories across multiple dimensions (semantic content, tags, timestamps, source context) with combined filtering and ranking. Supports complex queries that filter by metadata (date ranges, tags, source) while simultaneously performing semantic search, returning results ranked by relevance across all dimensions rather than simple keyword matching.