# Ouroboros

Turns a vague request into a testable spec through interview, crystallize, execute, evaluate. Useful when your agent keeps guessing requirements and redoing the same work.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed, Gemini CLI · ★ 6275
- Author: Q00
- License: MIT · policy=link-only · description only; the file stays with the author
- Source: https://github.com/Q00/ouroboros
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/mcp/ouroboros

## What it does

Ouroboros is a local-first runtime for AI coding. It runs a loop: interview, crystallize, execute, evaluate, evolve. You start with a rough idea and end with a Seed — a structured, testable statement of the task. The grading command and expected output stay out of the success contract, so the agent cannot tune its work to a known answer.

Under the hood there is a spec engine, a Ledger, an MCP layer and safety boundaries. The interview runs advisory lanes and reports an ambiguity score; the lower the score, the tighter the task. Install is one command, then `ooo setup` once inside your coding agent. On Windows the PowerShell script installs Git and uv for you, and Python is not needed.

Running the verify step yourself is the point — otherwise you hand the agent the answer key. The stack splits across three repos: core, domain plugins and a terminal client. Configuration differs per host, and for Cursor and GitHub Copilot it is a JSON block in settings rather than a terminal command.

## Requirements
- Needs: Python and uv (commands run via uvx)

## Install

### Claude Code (native platform)

**Platform CLI**
```bash
claude mcp add ouroboros -- uvx ouroboros-ai
```
uvx runs the command named after the package; if the README names another one, use uvx --from ouroboros-ai <command>

### Codex

**Platform CLI**
```bash
codex mcp add ouroboros -- uvx ouroboros-ai
```
uvx runs the command named after the package; if the README names another one, use uvx --from ouroboros-ai <command>

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "ouroboros": {
            "command": "uvx",
            "args": [
                "ouroboros-ai"
            ]
        }
    }
}
```
uvx runs the command named after the package; if the README names another one, use uvx --from ouroboros-ai <command>

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "ouroboros": {
            "type": "stdio",
            "command": "uvx",
            "args": [
                "ouroboros-ai"
            ]
        }
    }
}
```
uvx runs the command named after the package; if the README names another one, use uvx --from ouroboros-ai <command>

### Gemini CLI

**Platform CLI**
```bash
gemini mcp add ouroboros uvx ouroboros-ai
```
uvx runs the command named after the package; if the README names another one, use uvx --from ouroboros-ai <command>

## When to use it
- turn a vague idea into a testable spec
- stop an agent from tuning code to a known answer
- replay a successful agent run and inspect what happened
- cut ambiguity before the work starts, not after
- compose a domain workflow from core primitives

## Example prompts
- Interview me about this task and show the ambiguity score
- Crystallize this description into a Seed and check it is testable
- Run the loop: interview, crystallize, execute, evaluate

## Similar
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NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md