MCP Server Memo

Overview

MCP Server Memo is a lightweight Model Context Protocol (MCP) server that acts as a persistent memory layer for large‑language models such as Claude. It allows an AI assistant to store, retrieve, and browse detailed session summaries without relying on external databases. By writing each summary as a file on the local filesystem, it keeps the deployment footprint small while still offering full version control of conversation history.

The server solves a common pain point for developers building AI‑driven workflows: the lack of an easy, standards‑compliant way to persist conversational context. Without a memory store, each interaction is stateless and the model must be re‑informed from scratch. MCP Server Memo bridges that gap by exposing three core tools—upsertSummary, getSummaryTool, and listSummariesTool—that let client applications create new snapshot versions, fetch the most recent view, or enumerate all sessions. Because every update is written as a new file with an embedded timestamp, the system preserves every historical version automatically, enabling time‑ordered playback or audit trails.

Key capabilities include:

• Versioned history: Every call to upsertSummary appends a new file; previous versions remain untouched, making rollbacks and chronological analyses trivial.
• Time‑ordered organization: Files are named with timestamps, so listing them yields a natural chronological order without extra metadata.
• Tagging and filtering: Sessions can be annotated with arbitrary tags, which are respected by listSummariesTool for targeted queries.
• Lightweight persistence: No database engine is required—just a directory on disk—so the server can run in constrained environments or as part of a local development stack.
• MCP compliance: The tool interfaces adhere to the MCP specification, allowing any MCP‑aware client (Claude, OpenAI, etc.) to invoke them seamlessly.

In practice, developers use MCP Server Memo in scenarios such as:

• Customer support bots that need to recall previous interactions across multiple sessions for context continuity.
• Knowledge‑base assistants that index and retrieve long, structured summaries of training data or internal documents.
• Audit‑ready chat systems where every conversational change must be traceable for compliance purposes.
• Rapid prototyping environments where a full‑blown database is overkill but persistent state is still required.

Integration into an AI workflow is straightforward: the assistant calls after each turn to save a new snapshot, uses when it needs the latest context (e.g., to refine a response), and can let users browse their past conversations via . Because the server operates over HTTP and follows MCP, any client that supports the protocol can plug in without custom adapters. The result is a robust, versioned memory layer that scales from local scripts to production deployments while keeping the operational complexity minimal.