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MindCore Memory MCP

@woshilaohei

代码仓库主页

关于 MindCore Memory MCP

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

基本信息

分类

记忆与知识

传输方式

stdio

发布者

woshilaohei

提交者

woshilaohei

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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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