Chen's AI Copy
# Chen's AI Copy
Personal digital twin MCP (Model Context Protocol) server. Provides Chen's experience, skills, resume preferences, learnings, and interested companies to AI assistants across projects.
**Repo:** [github.com/shenchensucc/chens-ai-copy](https://github.com/shenchensucc/chens-ai-copy)
## What It Does
- **get_context** — Retrieve experience, skills, preferences, learnings, or interested companies
- **add_learning** — Record new learnings, patterns, or insights from project work
- **update_preference** — Update resume format, communication style, or other preferences
- **add_interested_company** — Add companies to track for job applications
## Quick Start
### Install from GitHub (new machine)
```bash
git clone https://github.com/shenchensucc/chens-ai-copy.git
cd chens-ai-copy
npm install
npm run build
```
### Add to Cursor
Add to `~/.cursor/mcp.json` (Windows: `C:\Users\<you>\.cursor\mcp.json` or Cursor Settings > MCP):
```json
{
"mcpServers": {
"chens-ai-copy": {
"command": "node",
"args": ["C:/path/to/chens-ai-copy/dist/index.js"]
}
}
}
```
Use the full path to your cloned `chens-ai-copy` folder.
### Sync on every Cursor start
The MCP uses `sync-and-run.ps1` which runs `git pull` before starting, so you always get the latest when opening Cursor. Ensure the MCP config points to the script:
```json
{
"chens-ai-copy": {
"command": "powershell",
"args": ["-NoProfile", "-ExecutionPolicy", "Bypass", "-File", "D:/2026/2026-03 Chens-ai-copy/sync-and-run.ps1"]
}
}
```
### Setup on another machine
1. Clone: `git clone https://github.com/shenchensucc/chens-ai-copy.git D:\2026\2026-03 Chens-ai-copy`
2. Run: `cd D:\2026\2026-03 Chens-ai-copy && npm install && npm run build`
3. Add the MCP config above to `~/.cursor/mcp.json` (adjust path if different)
## Data Structure
| File | Content |
|------|---------|
| `data/preferences.json` | Resume format, communication style, cover letter structure |
| `data/experience.json` | Work history, education, certifications |
| `data/skills.json` | Technical, soft, and domain skills |
| `data/learnings.json` | Project-derived learnings and patterns |
| `data/interested_companies.json` | Companies and roles of interest |
## Updating From Other Projects
When working in any project, ask the AI to:
- "Add this to my digital twin: [learning]"
- "Remember that I prefer [preference]"
- "Update my context with what we learned from this project"
The AI will call `add_learning` or `update_preference` to keep your context current.
## Environment Variables
- `CHENS_AI_COPY_DATA_PATH` — Override data directory (default: `./data` relative to package)
## Sync with GitHub
```bash
git pull # get updates
git push # push changes
```
## License
MIT
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: adding companies, adding learnings, retrieving full context, and updating preferences. The descriptions explicitly differentiate their use cases, making it impossible to confuse them.
The tools follow a consistent verb_noun pattern (add_interested_company, add_learning, get_context, update_preference), with all using snake_case. The only minor deviation is that 'get_context' uses 'get' while others use 'add' or 'update', but this is semantically appropriate and still maintains readability.
With 4 tools, this server is well-scoped for managing personal context and preferences. Each tool serves a specific function in this domain, and there are no extraneous or missing tools for the apparent purpose of maintaining Chen's background information.
The tool set covers the core CRUD operations for Chen's context: adding items (companies/learnings), retrieving context, and updating preferences. A minor gap is the lack of deletion tools (e.g., remove_company or remove_learning), but agents can likely work around this by updating preferences or context as needed.