This MCP server provides personal memory and automatic preference learning for Claude, enabling it to remember your workflow preferences across sessions and generate AI assistant instruction files.
Core Capabilities:
Automatic Preference Learning: Detects and stores user corrections (e.g., "use uv not pip") in real-time from natural language, learning tool preferences, workflow patterns, code styles, and project conventions
Memory Management: Add, search, and list knowledge episodes; export entire knowledge base to markdown for backup or sharing
Semantic Search: Uses AgentDB vector memory for ultra-fast (<1ms) semantic search with intelligent understanding
AI Standards Generation: Auto-generates instruction files (CLAUDE.md, GitHub Copilot instructions, Cursor rules) from existing project configs (.editorconfig, .prettierrc, ESLint, pyproject.toml, etc.)
CLAUDE.md Integration: Get AI-generated suggestions for updates based on learned patterns and automatically apply preferences with backup creation
Preference Analysis: Retrieve learned preferences with confidence scores, filterable by category (python, javascript, git, docker, testing, general)
Tool Execution Logging: Track tool usage patterns to learn from corrections and behaviors
100% Local Operation: All data stored in
~/.mcp-standards/with no cloud dependencies, telemetry, or privacy concernsSimple Setup: 5-minute integration with Claude Desktop via MCP protocol
Reads .editorconfig files to automatically extract formatting standards like indentation and line endings for AI assistant configuration
Analyzes ESLint configuration files to extract linting rules and JavaScript/TypeScript coding standards
Generates GitHub-specific instruction files for AI assistants and manages project-based configuration standards
Generates and updates Copilot instruction files (.github/copilot-instructions.md) based on learned patterns and project configurations
Detects Jest testing framework for JavaScript projects in configuration analysis
Analyzes package.json files to detect JavaScript/TypeScript project dependencies and build configurations
Detects Poetry configuration in Python projects for package management preferences
Parses Prettier configuration files to extract formatting rules, quotes preferences, and code style standards
Identifies pytest as the testing framework for Python projects in auto-generated standards
Reads pyproject.toml files to extract Python project configuration, dependencies, and tooling preferences
Parses Cargo.toml files to extract Rust project configuration and dependencies for AI assistant guidance
Uses SQLite database with FTS5 for storing learned patterns, preferences, and providing full-text search capabilities across knowledge base
Identifies Vitest as the testing framework for modern JavaScript/TypeScript projects
Detects Yarn as the package manager preference for JavaScript/TypeScript projects
MCP Standards - Personal Memory for Claude
โ ๏ธ ARCHIVED PROJECT: This repository is archived and provided for reference only. The project was experimental and served as a proof-of-concept for automatic preference learning with AgentDB vector memory. See ARCHIVE.md for complete details.
Make Claude remember YOUR preferences automatically. Zero config, zero manual steps.
๐ฏ What This Does
Stop repeating yourself to Claude. This MCP server learns your preferences automatically:
One correction. Forever remembered.
โก Quick Start (5 Minutes)
1. Install
2. Configure Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
โ ๏ธ Replace
3. Restart Claude Desktop
Quit and relaunch Claude Desktop.
4. Test It!
That's it! You're done. ๐
๐ง How It Works
Automatic Learning
Just correct Claude naturally - it learns automatically:
What Gets Remembered
โ Tool preferences: "use uv not pip", "prefer yarn over npm" โ Workflow patterns: "run tests before commit" โ Code style: "use TypeScript for new files" โ Project conventions: "follow PEP 8"
Categories (Auto-Detected)
python- Python/pip/uv preferencesjavascript- npm/yarn/pnpm preferencesgit- Git workflow preferencesdocker- Docker/container preferencestesting- Test framework preferencesgeneral- Everything else
๐ Features
Feature | Status |
Auto-detection | โ Detects "use X not Y" automatically |
Semantic search | โ <1ms with AgentDB (150x faster than SQLite) |
Cross-session | โ Preferences persist forever |
Zero config | โ Works out of the box |
100% local | โ No cloud, all private |
Simple | โ 5-minute setup |
๐ Documentation
Quick Start Guide - Detailed setup instructions
Validation Checklist - Testing & troubleshooting
Implementation Plan - Technical details
Skills Guide - How to use in Claude
๐ ๏ธ MCP Tools Available
Personal Memory (Simple Version)
Config Standards (Bonus)
๐ Architecture
Simple & Fast
Technologies
AgentDB - Ultra-fast vector memory (<1ms search)
Python - MCP server (async)
SQLite - Fallback storage
MCP Protocol - Claude Desktop integration
100% Local - No cloud dependencies
๐ Project Structure
Clean. Simple. Works.
๐งช Testing
Run automated validation:
Expected output:
๐ Troubleshooting
Setup fails
Claude Desktop doesn't connect
Preferences not remembered
Check that:
Server is running (check Claude Desktop MCP status)
Corrections use clear phrases ("use X not Y")
AgentDB path exists:
~/.mcp-standards/agentdb
More help: See Validation Checklist
๐ฏ Performance
Metric | Value |
Setup time | <5 minutes |
Server startup | <2 seconds |
Search speed | <1ms (AgentDB HNSW) |
Detection | Real-time (async) |
Storage | <10ms |
Memory usage | ~50MB (embedding model) |
150x faster than SQLite. Zero lag.
๐ Privacy
โ 100% local - Everything stored in
~/.mcp-standards/โ No cloud - No external API calls
โ No telemetry - No data collection
โ Your data - You control everything
๐ What Changed (v2 Simple)
We removed all the complexity:
v1 (Old) | v2 Simple (New) |
Manual MCP calls (4-5 steps) | โ Automatic (zero steps) |
SQLite keyword search (50ms+) | โ AgentDB vector search (<1ms) |
No semantic matching | โ Semantic understanding |
Complex setup | โ 5-minute setup |
6,000+ LOC | โ ~950 LOC |
Result: 80% less code, 100x better UX
๐ค Contributing
This is a personal side project. If you want to contribute:
Try it yourself first
Open an issue describing what you want to add
Wait for feedback before writing code
Please don't: Submit large PRs without discussion first.
๐ License
MIT License - See LICENSE file
๐ Credits
Built with inspiration from:
AgentDB - Ultra-fast vector memory
Context Engineering Guide - Minimal CLAUDE.md principles
Gunnar's approach - Simple side projects that solve personal problems
๐ฌ Questions?
Q: Why not just use v1? A: v1 requires 4-5 manual MCP calls per correction. v2 is zero-touch.
Q: Do I need AgentDB?
A: Yes, but it's installed automatically via npm run setup.
Q: Is my data private?
A: 100% local. Everything stored in ~/.mcp-standards/. No cloud.
Q: What if I want the old version?
A: Use src/mcp_standards/server.py instead of server_simple.py.
๐ Next Steps
Stop repeating yourself. Start remembering automatically. ๐ฏ
๐ฆ Archive Status
This project is archived. See ARCHIVE.md for:
Complete project status and achievements
Security audit results
Full documentation index
Lessons learned and technical insights
How to use this repository as reference
Made with โค๏ธ by keeping it simple