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eidetic-works/nucleus-mcp

by Various

Nucleus MCP server by Eidetic Works — persistent memory and governance for Claude, Cursor, Windsurf — file-based, no vendor lock-in.

E

MCP

eidetic-works/nucleus-mcp

Added 26 June 2026

#agent-os #ai-agents #ai-tools #claude #cursor #mcp #mcp-server #model-context-protocol

Overview

An MCP server that provides persistent memory and governance for AI coding assistants including Claude, Cursor, and Windsurf. Uses file-based storage to retain context and rules across sessions, avoiding vendor lock-in. Written in Python and open source.

Best for

Best for
Developers using Claude, Cursor, or Windsurf who want local, persistent memory and governance without vendor lock-in

Use cases

  • Retain long-term project context across Claude, Cursor, and Windsurf sessions
  • Enforce coding guidelines and governance rules consistently in AI interactions
  • Store session memory locally without relying on external cloud services

Notes

An MCP server that provides persistent memory and governance for AI coding assistants including Claude, Cursor, and Windsurf. Uses file-based storage to retain context and rules across sessions, avoiding vendor lock-in. Written in Python and open source.

3 stars on GitHub. Last updated 2026-06-26. Licensed MIT.

Use cases

  • Retain long-term project context across Claude, Cursor, and Windsurf sessions
  • Enforce coding guidelines and governance rules consistently in AI interactions
  • Store session memory locally without relying on external cloud services

Pros

  • Vendor-independent, works across multiple AI coding tools
  • Simple file-based persistence with no external dependencies
  • Open source Python implementation for easy customization

Cons

  • Low adoption (3 stars) suggests limited community and testing
  • File-based storage may not scale well for large teams or complex projects
  • Requires manual setup and configuration by the developer

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Vendor-independent, works across multiple AI coding tools
  • Simple file-based persistence with no external dependencies
  • Open source Python implementation for easy customization

Cons

  • Low adoption (3 stars) suggests limited community and testing
  • File-based storage may not scale well for large teams or complex projects
  • Requires manual setup and configuration by the developer