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.
MCP
eidetic-works/nucleus-mcp
Added 26 June 2026
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
How to use
Install
npx -y nucleus-mcp Tools exposed
eu-dorasg-mas-trmus-soc2global-default
Tested with
Claude Desktop, Claude Code, Cursor, Windsurf, ChatGPT
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
Open-source & AI alternatives
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Pairs with
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