Set Up an MCP Memory Server for Any AI Tool in 5 Minutes

One Memory Server. Every AI Tool You Use.

You use Claude Code for backend work. Cursor for frontend. ChatGPT Desktop for brainstorming. VS Code Copilot for quick edits. Maybe Windsurf when you want a different perspective. The tools are excellent. The problem is that each one has amnesia — and none of them talk to each other.

You explain your project’s architecture to Claude Code. Twenty minutes later, you switch to Cursor and explain it again. The database schema, the naming conventions, the deployment target, the testing strategy — repeated for every tool, every session.

An MCP memory server eliminates this problem entirely. One memory server, connected to all your AI tools through the Model Context Protocol. Store a memory anywhere. Recall it everywhere. No duplication. No re-explaining. No context lost between tools.

What Is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard created by Anthropic for connecting AI tools to external capabilities. Think of it as a universal adapter. Instead of every AI tool implementing its own integrations with every external service, MCP provides a single protocol that any tool can use to communicate with any server.

For memory, this is transformative. Before MCP, giving an AI tool memory meant building a custom integration for that specific tool. Memory for Claude Code was different from memory for Cursor was different from memory for VS Code. Each integration was separate, each database was separate, and memories were siloed.

With MCP, a single memory server speaks one protocol that all compatible tools understand. The memory server does not need to know which tool is calling it. Claude Code and Cursor send the same recall request and get the same response. The memory is truly shared.

As of February 2026, MCP is supported natively by Claude Code, Cursor, Windsurf, VS Code (via extensions), ChatGPT Desktop, Codex CLI, Gemini CLI, JetBrains AI Assistant, and a growing list of tools. The protocol is open source, and adoption is accelerating.

Why a Memory MCP Server Matters

Without shared memory, every AI tool you use is an isolated island. Context built in one tool does not transfer to another. The practical impact hits daily:

Context duplication. You store your project conventions in Claude Code’s memory. You store them again in Cursor’s context. You add them to a .cursorrules file. You put them in CLAUDE.md. You maintain the same information in four places, and they inevitably drift out of sync.

Session fragmentation. You debug an issue in Claude Code, identify the root cause, but need to switch to Cursor to fix it. The debugging context — the error messages, the hypotheses, the stack traces you explored — is locked in the Claude Code session. You start over in Cursor.

Knowledge loss at tool boundaries. Every time you switch tools, you lose context. Over a work day with multiple tool switches, the accumulated context loss is substantial. You spend more time re-establishing context than you do writing code.

An MCP memory server is the single source of truth. Store a memory once. Access it from any tool. Switch between tools without losing a single piece of context.

Setting Up Your MCP Memory Server

npm install -g superlocalmemory

superlocalmemory --version

{
  "mcpServers": {
    "superlocalmemory": {
      "command": "superlocalmemory",
      "args": ["--mcp"]
    }
  }
}

{
  "mcpServers": {
    "superlocalmemory": {
      "command": "superlocalmemory",
      "args": ["--mcp"]
    }
  }
}

Cross-Tool Memory in Action

Here is what cross-tool memory looks like in a real workflow:

Morning — Claude Code session. You are working on a new API endpoint. You store a memory:

Remember: The user authentication API uses JWT with RS256 signing.
Tokens expire after 1 hour. Refresh tokens are stored in httpOnly cookies
with a 30-day expiry. The signing key is in AWS Secrets Manager.

Afternoon — Cursor session. You switch to Cursor to build the frontend that calls this API. You ask:

Recall what you know about our authentication approach

Cursor retrieves the memory you stored in Claude Code that morning. It knows about the JWT configuration, the cookie strategy, the token expiry. You did not re-explain anything.

Later — VS Code session. You open VS Code to write integration tests. You ask:

Recall our API authentication details for writing tests

Same memory. Same context. Third tool. Zero repetition.

This is not a theoretical scenario. This is what happens when all your tools share a single memory server through MCP. The memory does not belong to any one tool — it belongs to you.

The 18 MCP Memory Tools

SuperLocalMemory exposes 18 tools through the MCP protocol. Every connected tool has access to all of them:

Core Operations

• remember — Store new memories with automatic tagging and indexing
• recall — Search and retrieve relevant memories
• forget — Remove specific memories permanently

Knowledge and Learning

• search — Full-text search across all memories
• get_behavioral_patterns — View learned coding preferences and patterns

Lifecycle Management

• get_lifecycle_status — View memory lifecycle states and transitions
• compact_memories — Trigger manual lifecycle transitions
• set_retention_policy — Configure retention rules for compliance

Compliance and Audit

• report_outcome — Record action outcomes for behavioral learning
• audit_trail — Query the compliance audit log

System

• status — Check system health and memory statistics
• configure — Update runtime configuration

Each tool accepts structured parameters and returns structured results. The MCP protocol handles serialization and communication. From the user’s perspective, you use natural language — “remember this,” “recall that” — and the AI tool translates your request into the appropriate MCP tool call.

Performance: What to Expect

Speed matters for interactive coding workflows. Here is what the MCP memory server delivers:

Search latency: under 11 milliseconds. Memory recall completes in single-digit milliseconds for typical databases (under 10,000 memories). This includes full-text search, relevance ranking, and result formatting. You do not notice the memory lookup — it is faster than the AI tool’s own response generation.

Storage efficiency. The local database uses embedded storage with full-text indexing. A database with 5,000 memories typically occupies 10-20MB of disk space. With lifecycle management enabled, cold and archived memories are compressed further.

Startup time: under 2 seconds. The MCP server starts when your AI tool launches and stays running for the duration of your session. The first launch initializes the database if needed. Subsequent launches connect to the existing database instantly.

Concurrent access. Multiple AI tools can connect to the same memory server simultaneously. The database handles concurrent reads safely. If you have Claude Code and Cursor open at the same time, both have access to the same memories without conflicts.

Why Local MCP Beats Cloud Memory APIs

Cloud memory APIs exist. They work. But for a developer’s personal memory server, local MCP has structural advantages:

Zero latency penalty. Every cloud API call adds network round-trip time. Local MCP communicates through local process pipes — no network involved.

Zero cost. No API usage fees. No monthly subscriptions. No tier limits. The server runs on your machine using your resources.

Zero privacy risk. Your memories — containing code context, architecture decisions, and project details — never traverse a network. They exist only on your filesystem.

Zero dependency on external services. Cloud APIs go down. Endpoints change. Authentication tokens expire. Pricing changes. A local MCP server depends on nothing beyond your machine.

For production agent systems that need cloud scale, cloud memory APIs serve a different purpose. But for a developer’s personal memory layer — the thing you rely on for every coding session — local MCP is the architecture that makes sense.

Troubleshooting

Tool does not show SuperLocalMemory as connected: Verify the MCP configuration JSON is valid. Run superlocalmemory --mcp in your terminal to confirm the server starts without errors. Check the tool’s documentation for the exact location of its MCP settings file.

Memory stored in one tool is not visible in another: Both tools must point to the same SuperLocalMemory instance. If you are using profile flags (--profile work), ensure all tools use the same profile. Different profiles create separate databases.

Server fails to start: Check that Node.js 18+ is installed (node --version). Verify the global install succeeded (which superlocalmemory). If the binary is not on your PATH, add $(npm bin -g) to your shell configuration.

For tool-specific setup guides and detailed MCP configuration, visit the MCP documentation page.

Get Started

One install. Every AI tool you use. Shared memory across all of them.

npm install -g superlocalmemory

Five minutes to set up. After that, every AI tool you use shares the same context. Store a memory in Claude Code at 9 AM. Recall it in Cursor at 2 PM. Reference it in VS Code at 5 PM. One memory server. Zero repetition. Zero context lost.