Agent Memory Means Two Different Things, and Answer Engines Hand You the Wrong One

Description and use

Developers using multiple AI assistants lack a shared memory, causing repeated context re‑introduction and inconsistent facts. Employing the Model Context Protocol (MCP) with a hosted memory service lets all tools access the same personal memory, boosting productivity while introducing latency and privacy concerns.

Problem

Users working across multiple AI tools (Claude Code, Cursor, ChatGPT) lack a shared memory that persists across tool boundaries, causing repeated context re‑introduction and inconsistent knowledge.

Outcome

Cross‑tool personal memory reduces context re‑introduction, ensures consistent facts across assistants, and improves developer productivity, but introduces network latency and vendor dependency.

Workflow

• 1. Deploy a hosted MCP server and a vector store (e.g., pgvector).
• 2. Configure each tool (Claude Code, Cursor, ChatGPT) to use the MCP memory_write/memory_read tools via its plugin or system prompt.
• 3. Authenticate with a single identity (API key or OAuth) so all tools resolve to the same memory space.
• 4. Add standing instructions in each tool to query memory before acting (e.g., “Recall personal context before responding”).
• 5. Record decisions with reasons and validity windows to maintain authority and prevent stale facts.

Evidence

The Model Context Protocol changed the shape of the problem: one server can now expose the same tools to Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, and a growing list of others.

Source

Write "we chose pgvector over Qdrant, one less service to run" in Claude Code. Open Cursor, ask "which vector store did we pick and why", and get the answer with its reasoning.

Source