MCP CLI Tool for Structured Agent Workflows

A local-first CLI tool converting PRD files into persistent execution graphs (SQLite) for structured agentic workflows.

MCP CLI Tool for Structured Agent Workflows

The MCP CLI Tool is a developer-focused utility designed to streamline the creation and execution of structured agentic workflows. It operates locally, transforming Product Requirements Documents (PRDs) into persistent execution graphs stored in SQLite databases. This approach offers a robust and reproducible method for managing complex AI agent interactions, ensuring consistency and facilitating debugging.

What it Does

This tool takes your PRD files as input and generates a structured, executable representation of your agent's workflow. Instead of managing ad-hoc scripts or complex state machines, you define your requirements in a clear, document-oriented format. The MCP CLI then translates these requirements into a persistent SQLite database, which acts as the execution graph. This graph details the sequence of agent actions, dependencies, and decision points, enabling predictable and repeatable execution of your AI agents.

Key Features

Local-First Operation: All processing and graph generation occur on your local machine, enhancing privacy and control.
PRD to Graph Conversion: Directly translates human-readable PRD files into machine-executable graphs.
Persistent SQLite Storage: Execution graphs are stored in SQLite databases, allowing for easy inspection, versioning, and modification.
Structured Agentic Workflows: Facilitates the creation of well-defined, multi-step agent processes.
Developer-Centric: Designed with developers in mind, offering a technical and efficient workflow.

Who it's For

The MCP CLI Tool is intended for AI developers, researchers, and engineers who are building complex agentic systems. If you are working with multiple AI agents, require deterministic execution, or need a structured way to manage the logic and state of your AI applications, this tool will be highly beneficial. It's particularly useful for projects where reproducibility, debugging, and clear workflow definition are critical.