Which integrations are available for this server?

Provides persistent vector memory storage and semantic search capabilities for the Windsurf editor, allowing for project-specific context retrieval and documentation ingestion.

How do I use MCP Memory Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Memory Server search for architectural decisions in project-thaama" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Memory Server

A persistent vector memory server for Windsurf, VS Code, and other MCP-compliant editors.

Features

Local Vectors: Uses LanceDB and all-MiniLM-L6-v2 locally. No API keys required.
Persistence: Memories are saved to disk (./mcp_memory_data).
Isolation: Supports multiple projects via project_id.

Installation

You need Python 3.10+ installed.

Setup Virtual Environment It's recommended to use a virtual environment to avoid conflicts.
python3 -m venv .venv source .venv/bin/activate
Install Dependencies
pip install -e .

Configuration

Add this to your mcpServers configuration (e.g., in ~/.codeium/windsurf/mcp_config.json or VS Code MCP settings).

Windsurf / VS Code Config

Replace /ABSOLUTE/PATH/TO/... with the actual path to this directory.

{ "mcpServers": { "memory": { "command": "/ABSOLUTE/PATH/TO/mcp-memory-server/.venv/bin/python", "args": [ "-m", "mcp_memory.server" ], "env": { "MCP_MEMORY_PATH": "/ABSOLUTE/PATH/TO/mcp-memory-server/mcp_memory_data" } } } }

Usage

Ingestion

Use the included helper script ingest.sh to ingest context files.

# General Usage ./ingest.sh --project <PROJECT_NAME> <FILES...>

Real Example (Project "Thaama"):

/ABSOLUTE/PATH/TO/mcp-memory-server/ingest.sh --project project-thaama \ /path/to/PROJECT_CONTEXT.md \ /path/to/DECISIONS.md \ /path/to/AI_RULES.md

Tools

The server exposes:

memory_search(project_id, q)
memory_add(project_id, id, text)

Troubleshooting

First Run: The first time you run it, it will download the embedding model (approx 100MB). This might take a few seconds.
Logs: Check the editor's MCP logs if connection fails.