Ouroboros

What Ouroboros MCP server does

The Ouroboros MCP server provides the MCP portion of an Agent OS for AI coding workflows. Its central process is specification-first: an agent or user starts with an underspecified request, works through an interview, turns the result into a structured Seed, executes the task, evaluates the result, and can evolve the workflow across generations.

The OS layer is responsible for the contract around that work. Actions are associated with the Seed and recorded as replayable events in a Ledger. This separates the execution engine from the individual model or coding client running the task. The repository describes the system as local-first and policy-bound, with safety boundaries around runtime execution.

How it works

Ouroboros treats clarity as an input to coding rather than relying on an agent to infer all requirements from a short prompt. Interview questions expose ambiguity around areas such as ordering and scope. The resulting Seed locks the intended specification before implementation begins.

During an interview, advisory lanes can run in parallel and return findings between rounds. The process ends with a structured submission and an ambiguity result; the README shows an example reaching a final ambiguity value of 0.15. After the specification stage, the runtime executes the task and the evaluation flow provides a verification gate rather than treating an apparently plausible result as sufficient.

The Ouroboros MCP server is the OS or kernel layer in a larger stack. Ourocode provides a terminal interface, while Ouroboros plugins compose core primitives into domain workflows. The core can also be used directly with supported command-line agents.

Setup and configuration

The repository provides installation commands for macOS, Linux, and WSL 2 through an install shell script. Windows PowerShell has a separate installer; the README says that path installs Git and uv and does not require Python. After installation, run ooo setup once inside the coding agent.

The README also presents a direct Python-package distribution through the ouroboros-ai project and identifies the command-line entry point as ooo. It does not specify required environment variables in the provided material. Separate runs can use separate hosts and intentionally different tasks while sharing the execution engine.

Tools and capabilities

Documented capabilities include:

• Conducting the Ouroboros interview flow through MCP.
• Running advisory lanes in parallel before interview submission.
• Turning a vague request into a structured, testable Seed.
• Recording Seed-bound actions as ledger events.
• Supporting replayable execution and a staged evaluation gate.
• Connecting the core workflow to command-line agent runtimes.

The README shows an MCP tool identifier named mcp__ouroboros__ouroboros_interview and describes submitting fan-out results between interview rounds. Exact schemas for these calls are not included in the supplied material.

Limitations and notes

Ouroboros is presented as a runtime and workflow layer, not as a domain application for a particular issue tracker, release system, or project-management product. Those use cases belong to the separate plugin layer, whose examples include PR operations, Jira synchronization, incidents, and releases.

The README names several hosts and agents, including Claude Code, Codex, OpenCode, Hermes, Gemini, Kiro, Copilot, Pi, OMP, Zcode, Goose, GJC, Antigravity, and Grok. Client-specific setup details are not provided here. The project also distinguishes itself from an unrelated repository with the same name that focuses on self-modifying autonomous memory; this project instead locks a specification before execution.