Simple Memory Extension MCP Server

Simple Memory Extension MCP Server

The Simple Memory Extension MCP Server fills a common gap in AI‑assistant workflows: the ability to persist and retrieve contextual information beyond the limited token window of a language model. When developers build agents that need to remember milestones, intermediate results, or domain‑specific facts across long sessions—such as during complex code generation, iterative design, or conversational storytelling—the server provides a lightweight, schema‑free store that can be queried on demand. By exposing simple key/value operations and semantic search, it lets agents act like a personal knowledge base that evolves with the conversation.

At its core, the server offers three classes of tools. First, Context Item Management lets an agent store arbitrary values under a key within a named namespace, fetch them later, or delete them when they’re no longer relevant. Second, Namespace Management gives developers the ability to isolate data for different projects or users by creating, listing, and deleting namespaces. Third, Semantic Search leverages the multilingual‑E5 embedding model to locate items by meaning rather than exact string matches, enabling fuzzy retrieval of related concepts or code snippets. The semantic pipeline automatically splits long texts into chunks, embeds them, and ranks matches by cosine similarity—making it possible to ask an agent for “all recent discussion about database schema changes” without specifying exact keys.

Developers integrate the server into their MCP‑enabled assistant simply by declaring the available tools in the client’s capability list. Once connected, an agent can issue commands such as or . The server responds with structured JSON, allowing the agent to decide how to incorporate the retrieved data into its next prompt. Because the memory store is persistent (SQLite backend), it survives agent restarts and can be shared across multiple assistants or users, making it ideal for collaborative coding sessions, knowledge‑base building, or long‑running task automation.

Real‑world scenarios that benefit from this MCP include:

• Iterative code generation where the assistant remembers previous function signatures and style guidelines.
• Project management bots that track milestones, task statuses, and stakeholder feedback across a project timeline.
• Customer support agents that retain user preferences and past interactions to provide personalized help.
• Research assistants that aggregate related literature snippets and recall them during writing or analysis.

What sets this server apart is its balance of simplicity and power. The API surface is intentionally minimal—just a handful of tools—yet it covers the essential operations for long‑term context handling. The built‑in semantic search removes the need for external vector stores or manual indexing, while the namespace feature keeps data organized and secure. For developers already familiar with MCP concepts, this server can be dropped into an existing workflow with no additional tooling or infrastructure overhead, giving AI assistants a robust memory layer that scales from single‑user experiments to production deployments.