IDE Memory MCP

Cross-IDE persistent memory for AI coding agents — your AI remembers every project, across every IDE.

The Problem

Every time you open a project in a new IDE or start a fresh AI conversation, your AI assistant forgets everything:

• What the project does
• Architecture decisions you've made
• What you're currently working on
• Your progress and milestones

You end up repeating yourself. Every. Single. Time.

The Solution

IDE Memory MCP gives AI coding agents a persistent memory layer that works across any IDE supporting the Model Context Protocol. Write project context once — the AI remembers it everywhere.

Cursor ←──→ IDE Memory MCP ←──→ VS Code
   ↑              ↓                 ↑
   └── same project memory ────────┘

Key Features

• Cross-IDE Memory — Project context persists across Cursor, VS Code, Windsurf, Claude Desktop, and any MCP-compatible IDE
• Context-Optimized — Default reads return a compact summary table, not a context-window-destroying content dump. The AI loads only what it needs.
• Smart Warnings — Automatically detects stale sections (>7 days), oversized content (>10k chars), and suggests pruning when memory gets old (>30 days)
• Agent Prompts — Built-in MCP prompts guide the AI on how to start sessions, bootstrap memory for new projects, and update memory after changes
• Smart Project Matching — Recognizes projects by path or git remote URL. Move folders, switch machines — your memory follows.
• Section-Based Storage — Organized into overview, decisions, active_context, progress + custom sections
• Append Mode — Add incremental updates without rewriting entire sections
• Version History — Previous content auto-saved before each overwrite (last 5 snapshots)
• Zero Config — Works out of the box. No database, no cloud, just local markdown files.

Quick Start

1. Install

# Using uv (recommended)
uv pip install ide-memory-mcp

# Using pip
pip install ide-memory-mcp

2. Auto-Configure Your IDE

Run the setup command to automatically detect and configure installed IDEs (Cursor, VS Code, Windsurf, Claude Desktop):

ide-memory-mcp setup

💡 Restart your IDE after running this command to activate the MCP server.

CLI Commands

The ide-memory-mcp package includes practical commands to manage your setup:

ide-memory-mcp setup

Auto-configure MCP for your IDEs.

ide-memory-mcp setup              # auto-detect + configure all
ide-memory-mcp setup --cursor     # configure only Cursor
ide-memory-mcp setup --vscode     # configure only VS Code
ide-memory-mcp setup --windsurf   # configure only Windsurf
ide-memory-mcp setup --claude     # configure only Claude Desktop
ide-memory-mcp setup --all        # configure all supported

ide-memory-mcp doctor

Health check your installation.

ide-memory-mcp doctor

Verifies server import, storage disk usage, projects count, and which IDEs are configured.

ide-memory-mcp status

Quick overview of all projects.

ide-memory-mcp status

Lists registered projects with section counts, total size, and last updated date.

{
  "mcpServers": {
    "ide-memory": {
      "command": "ide-memory-mcp"
    }
  }
}

{
  "mcpServers": {
    "ide-memory": {
      "command": "ide-memory-mcp"
    }
  }
}

{
  "mcpServers": {
    "ide-memory": {
      "command": "ide-memory-mcp"
    }
  }
}

{
  "mcpServers": {
    "ide-memory": {
      "command": "ide-memory-mcp"
    }
  }
}

Tools

IDE Memory MCP exposes 4 optimized tools to the AI agent:

init_project

Register or reconnect to a project. Call this first in every conversation.

init_project(projectPath, projectName?, gitRemoteUrl?)

• New project → creates memory storage, suggests next steps, mentions bootstrap_memory prompt
• Known project → reconnects, returns memory summary with smart warnings:
  • ⚠️ Empty sections that need filling
  • ⏰ Stale sections (>7 days) that should be updated
  • 📦 Large sections (>10k chars) that may need pruning
  • 🧹 Old sections (>30 days) suggesting a full prune

read_memory

Context-aware memory loading. Optimized to avoid flooding the AI's context window.

read_memory(projectIdOrPath, sections?, query?, maxChars?, history?, prune?)

💡 Recommended workflow:

1. read_memory(projectId) → get the summary table (~10 lines)
2. Decide which sections are relevant
3. read_memory(projectId, sections=["overview", "decisions"]) → load only what you need

write_memory

Write to one or more memory sections.

write_memory(projectIdOrPath, sections, append?, heading?)

• Overwrite mode (default): Replace entire section content. Previous content auto-saved to history.
• Append mode (append=True): Add timestamped entries without rewriting. Great for decisions and progress logs.

Tool description includes behavioral guidance — it tells the AI agent when to call it:

• After significant code changes
• When important decisions are made
• At the end of productive sessions
• When the user asks to "remember" something

manage_projects

List or delete projects.

manage_projects(action, projectIdOrPath?, confirm?)

MCP Prompts

IDE Memory MCP includes 3 built-in prompt templates that guide the AI agent through common workflows. These solve the "agent doesn't know when to use memory" problem.

start_session

When: Beginning of every conversation.

Guides the agent through: initialize project → read summary → load relevant sections → check for stale content → plan memory updates for end of session.

bootstrap_memory

When: First time using IDE Memory on an existing project, or when the user says "learn about this project".

Guides the agent through: analyze README & package files → write comprehensive overview → document architecture decisions → set active context → record progress.

update_memory

When: End of a productive session, after significant changes, or when the user says "save what we did".

Guides the agent through: read current memory → update active_context → append new decisions → update progress → update overview if needed → check if pruning is needed.

Memory Sections

Default sections created for every project:

Custom sections are fully supported — use any lowercase identifier:

write_memory(projectId, {"api_contracts": "...", "testing_notes": "..."})

Smart Warnings

The memory summary automatically includes actionable warnings:

Storage

All memory is stored as simple markdown files in ~/.ide-memory/projects/:

~/.ide-memory/
├── config.json              # Optional configuration
└── projects/
    └── <project_id>/
        ├── meta.json         # Project metadata + timestamps
        ├── overview.md
        ├── decisions.md
        ├── active_context.md
        ├── progress.md
        └── .history/         # Auto-saved snapshots
            ├── overview_20260314_120000.md
            └── decisions_20260314_130000.md

• No database required
• All files are human-readable markdown
• Easy to backup, version, or migrate
• No data ever leaves your machine

Configuration

Optional. Create ~/.ide-memory/config.json:

{
  "default_sections": [
    "overview",
    "decisions",
    "active_context",
    "progress"
  ]
}

Development

Prerequisites

• Python 3.11+
• uv (recommended) or pip

Setup

git clone https://github.com/prasanna-pmpeople/IDE-Memory-MCP.git
cd IDE-Memory-MCP
uv sync

Run the server

uv run ide-memory-mcp

Run tests

uv run pytest tests/ -v

Test with MCP Inspector

npx -y @modelcontextprotocol/inspector uv run ide-memory-mcp

See TESTING.md for the complete testing guide, including MCP Inspector walkthrough and cross-IDE battle testing.

Build the package

uv build

Install from built package

pip install dist/ide_memory_mcp-1.0.0-py3-none-any.whl

How It Works