Optimized Memory MCP Server v2

Overview

The Agentwong Optimized Memory MCP Server v2 is a lightweight, Python‑based implementation of the Model Context Protocol (MCP) designed to extend Claude Desktop with persistent, structured memory and custom tooling. It tackles the common challenge of maintaining conversational context across long interactions: without a dedicated memory store, an AI assistant can lose track of user preferences, project details, or prior decisions. This server solves that problem by offering a fully managed database backend and an API surface that Claude can query, update, and manipulate as if it were a native component of the model.

At its core, the server exposes three types of MCP resources—resources, tools, and prompts—each wired to a relational database. Resources represent long‑term facts or knowledge bases that the assistant can read; tools are executable actions (e.g., querying an API, running a script) that the assistant can invoke; prompts are reusable prompt templates that help shape responses. By exposing these through MCP, developers can treat them as first‑class citizens in the assistant’s workflow: a user can ask Claude to “fetch my last meeting notes” (resource read), “run the data‑cleaning script” (tool invocation), or “apply the formal tone prompt” (prompt selection). The server’s schema‑driven design ensures that updates to these entities are instantly visible to all connected clients, providing a single source of truth for multi‑session or multi‑assistant environments.

Key capabilities include:

• Persistent, relational storage: All resources, tools, and prompts are stored in a SQLite (or other SQL) database, enabling complex queries and versioning.
• Dynamic tool execution: Tools can be added or modified without redeploying the server; Claude can call them on demand, passing arguments and receiving structured results.
• Prompt templating: Prompts can include placeholders that the server fills with runtime data, allowing for context‑aware response generation.
• MCP compliance: The server follows MCP specifications, making it interoperable with any client that implements the protocol, not just Claude Desktop.

Typical use cases span a range of real‑world scenarios. In software development, a team can store code snippets, architecture diagrams, or API documentation as resources and let Claude retrieve them during pair‑programming sessions. In research, long‑term experiment logs can be queried by the assistant to provide historical context for new analyses. Marketing teams might maintain a library of brand guidelines (prompts) and run custom content‑generation tools that pull in latest campaign data. Because the server is modular, developers can plug in external services—such as a weather API or a database query tool—without touching the core MCP logic.

Integrating this server into an AI workflow is straightforward: a developer sets up the database, defines resources and tools via the MCP API or through the provided Python modules, and then points Claude Desktop to the server’s URL. From there, all interactions—whether conversational or task‑oriented—benefit from the shared memory and executable capabilities. The result is a more coherent, context‑rich assistant that can adapt to user needs over time and execute complex actions on demand.