MindCore Memory MCP

Name: MindCore Memory MCP
Author: woshilaohei

@woshilaohei

MindCore Memory MCP について

AI long-term memory MCP server with importance scoring and confidence calibration. Enables AI agents to store, search, and retrieve long-term memories with semantic similarity and importance-weighted recall.

#memory #ai #mcp #python #agent-tools

基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

公開者

woshilaohei

投稿者

woshilaohei

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": {
    "mindcore-memory": {
      "command": "python",
      "args": [
        "-m",
        "mindcore_memory"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Mindcore Memory Mcp?

Mindcore Memory Mcp is an AI long-term memory server that provides production-grade persistent memory for AI agents. It integrates with any MCP-compatible client (Claude Desktop, Cursor, Cline, etc.) via stdio transport and uses an embedded TinyDB database, requiring no external infrastructure.

How to use Mindcore Memory Mcp?

Install via pip (pip install mindcore-memory), then launch the MCP server with the mindcore-memory command. Configure the MCP client with the command python -m mindcore_memory.server in its settings. Once connected, call tools such as memory_store, memory_recall, memory_context, memory_update, memory_delete, and memory_stats to manage memories.

Key features of Mindcore Memory Mcp?

Zero external dependencies (embedded TinyDB)
Importance grading (1–4) and confidence scoring
Smart pruning and context window construction
Deduplication and ranking by relevance × importance × confidence
Six core tools: store, recall, context, update, delete, stats

Use cases of Mindcore Memory Mcp?

Maintain user preferences and schedules across AI agent sessions
Store and retrieve project architecture decisions without re-teaching
Build precise context windows for complex queries without overload
Accumulate knowledge over time for continuous learning agents

FAQ from Mindcore Memory Mcp

What does Mindcore Memory Mcp use for storage?

It uses TinyDB, an embedded database that stores memories locally as JSON files. No PostgreSQL, Redis, or vector database is required.

What are the runtime requirements?

Python 3.10 or higher. No external database or additional services are needed.

How are memories ranked when recalled?

Memories are ranked by a scoring engine that combines importance, confidence, recency, and relevance to the query.

What transport protocol does Mindcore Memory Mcp use?

It implements the Model Context Protocol (MCP) over stdio transport.

Is there any limit on the number of memories?

The

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