Vector Memory MCP Server

@Xsaven

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

作者

Xsaven

投稿者

Артем Охрамович

Vector Memory MCP Server について

Vector Memory MCP Server

#vector #memory

Vector Memory MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "vector-memory": {
      "command": "uvx",
      "args": [
        "vector-memory-mcp",
        "--working-dir",
        "/absolute/path/to/your/project"
      ]
    }
  }
}

Vector Memory MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Vector Memory MCP Server?

Vector Memory MCP Server is a secure, vector-based memory server for Claude Desktop that uses sqlite-vec and sentence-transformers. It provides persistent semantic memory capabilities to enhance AI coding assistants by remembering and retrieving relevant coding experiences, solutions, and knowledge.

How to use Vector Memory MCP Server?

Install via uvx (recommended) or from source. Configure Claude Desktop by adding a JSON entry pointing to the server and a working directory. Then use available tools such as store_memory, search_memories, list_recent_memories, get_memory_stats, clear_old_memories, get_by_memory_id, and delete_by_memory_id via natural language commands.

Key features of Vector Memory MCP Server

Semantic search using 384-dimensional embeddings
Persistent SQLite storage with vector indexing
Smart organization with categories and tags
Input validation, path sanitization, and resource limits
Fast embedding generation with sentence-transformers
Automatic deduplication via SHA-256 content hashing
Access tracking with counts and timestamps
Smart cleanup algorithm based on recency and importance

Use cases of Vector Memory MCP Server

Store and retrieve code patterns, bug fixes, and architecture decisions
Maintain team conventions, deployment procedures, and infrastructure knowledge
Capture learning insights, performance discoveries, and security learnings
Enable semantic search across past coding experiences and solutions

よくある質問

What is the technical stack of Vector Memory MCP Server?

It uses `sqlite-vec` for vector storage and similarity search, the `sentence-transformers/all-MiniLM-L6-v2` model for 384-dimensional embeddings, and the FastMCP framework.

What are the security limits?

Maximum memory content length is 10,000 characters, maximum total memories is 10,000 entries, maximum search results per query is 50, and maximum tags per memory is 10. Path validation blocks suspicious characters.

How does semantic search work?

Memories are converted into 384-dimensional vectors that capture semantic meaning. Search queries are embedded and compared using similarity scoring (0.6–1.0 range) to find relevant matches.

How can I install Vector Memory MCP Server?

You can install via `uvx` (recommended), from source using `uv`, or with `pipx`. Publishing to PyPI is in progress.

What dependencies are required?

Python 3.10 or higher (recommended 3.11), the `uv` package manager, and the Claude Desktop app.

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