The article describes how a team organized 118 AI tools on an MCP server, detailing their iterative process and challenges. They structured the tools into 19 files, grouped by domain, to effectively manage complexity.
The sprawl of AI tools in enterprises, driven by deployment speed, is creating escalating costs and risks such as duplicated engineering effort, security exposure, and operational opacity. It is crucial for every enterprise to implement its own shared AI tool registry to mitigate these issues.
The author details the challenges of managing numerous AI coding tools, each with unique configurations and setups, which creates an inconsistent and unmanageable workflow. This struggle transforms product research into a frustrating experience of constantly reconfiguring agents and copying skills.
The author created "tool-manage", a CLI tool, to solve the problem of managing numerous small command-line utilities. This tool provides a local, SQLite-backed registry to store metadata, help output, and manage various CLI commands efficiently.