# vardoger

It studies local assistant conversations and turns recurring work preferences into system-prompt additions. Use it to tailor an AI coding assistant to your workflow.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed, Gemini CLI · ★ 5
- Author: dstrupl
- License: Apache-2.0 · policy=link-only · description only; the file stays with the author
- Source: https://github.com/dstrupl/vardoger
- Updated: 2026-09-27
- HTML: https://affpapa.org/en/nevibecode/mcp/vardoger

## What it does

vardoger parses supported conversation files stored on your machine. The host assistant’s model identifies patterns in your communication, tools and work habits, then produces additions for the platform’s system instructions. You need Python 3.11+ and `pipx`. Analysis runs through the assistant you invoke, so selected conversation excerpts are subject to that model provider’s data policy.

On its first run, vardoger reads the full local history; it does not apply a time limit by default. Later runs use checkpoints to process conversations that are new or changed. History, checkpoints and generated rules stay on your machine, and the server has no backend or telemetry. Keep the provider’s policy in mind: the excerpts selected for analysis go through the host assistant.

## Requirements
- Needs: Python and uv (commands run via uvx)
- Environment variables: `VARDOGER_MCP_PLATFORM` — keys and tokens: ask the human for the values, never commit them or put them in shared files.

## Install

### Claude Code (native platform)

**Platform CLI**
```bash
claude mcp add --env VARDOGER_MCP_PLATFORM=<VARDOGER_MCP_PLATFORM> --transport stdio vardoger -- uvx vardoger
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from vardoger <command>

### Codex

**Platform CLI**
```bash
codex mcp add vardoger --env VARDOGER_MCP_PLATFORM=<VARDOGER_MCP_PLATFORM> -- uvx vardoger
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from vardoger <command>

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "vardoger": {
            "command": "uvx",
            "args": [
                "vardoger"
            ],
            "env": {
                "VARDOGER_MCP_PLATFORM": "<VARDOGER_MCP_PLATFORM>"
            }
        }
    }
}
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from vardoger <command>

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "vardoger": {
            "type": "stdio",
            "command": "uvx",
            "args": [
                "vardoger"
            ],
            "env": {
                "VARDOGER_MCP_PLATFORM": "<VARDOGER_MCP_PLATFORM>"
            }
        }
    }
}
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from vardoger <command>

### Gemini CLI

**Platform CLI**
```bash
gemini mcp add -e VARDOGER_MCP_PLATFORM=<VARDOGER_MCP_PLATFORM> vardoger uvx vardoger
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from vardoger <command>

## When to use it
- derive work preferences from local conversation history
- add tailored instructions to an assistant’s settings
- refresh personalization from new conversations
- compare conversation-quality metrics before and after personalization

## Example prompts
- Review my local conversations and draft personalized instructions for the assistant.
- Refresh my assistant instructions using the conversations added since the last run.
- Compare conversation-quality metrics from before and after personalization.

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