Technology Apr 25, 2026 · 3 min read

Taming MCPs, Skills, and Agent Chaos with ToolHive

I use a lot of AI coding tools: OpenCode, Antigravity, Claude Code, Codex, Gemini CLI, Pi, Zed, GitHub Copilot, and probably others too. I'm having a ton of fun vibe coding and learning each of these systems. But ultimately, I need to understand these tools because my job is helping developers make...

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DEV Community
by Ryan Swift
Taming MCPs, Skills, and Agent Chaos with ToolHive

I use a lot of AI coding tools: OpenCode, Antigravity, Claude Code, Codex, Gemini CLI, Pi, Zed, GitHub Copilot, and probably others too. I'm having a ton of fun vibe coding and learning each of these systems. But ultimately, I need to understand these tools because my job is helping developers make sense of this stuff.

I'm lucky to have a (reasonably) unlimited AI budget. But managing what feels like 30 different tools has become a pain. Every tool wants its own config, MCP setup, skills folder, auth, etc.

Trying every AI dev tool has stopped feeling like product research and started feeling like I'm torturing a tiny fleet of confused robots.

A dream of consistency

I am painfully organized at times. My sprawling collection of agents feels out of hand. It is not strictly difficult to reconfigure an MCP server or drop in a skill I need, but I find myself wanting a more consistent experience across every tool.

If I (read: my agents) create a skill, I do not want to copy it into six different folders. This has been the real pain point for me:

AI tool sprawl is the new dotfile hell.

What I tried (today)

I've bumped into the Stacklok/ToolHive folks at a couple of conferences and finally had time to try out their project today. Initial thoughts are pretty positive.

I ended up using their CLI and desktop app, ToolHive Studio. My hope was that ToolHive could become the control plane for running MCPs, storing auth, and distributing skills. Most importantly, I wanted one command that could register an MCP or skill across all of my coding agents.

It mostly worked!

There was still plenty of configuration and testing to do, but I am pretty happy with where I landed. I have the start of a governable setup that works across my agents.

List of ToolHive managed MCPs and Clients

I ran into a Podman configuration issue with their official package. Skills are still a beta/alpha feature and needed to be enabled to be used inside Studio. Their built-in playground worked well for testing.

A list of repositories pulled from the GitHub MCP inside ToolHive's Playground.

Takeaways

If you're using one AI coding tool, config is an annoying chore. The agent can probably configure itself.

When you're using ten coding tools, config is a nightmare.

ToolHive made my system governable and solved a real problem for me right now. I needed a control plane. Not perfect, but useful in exactly the way I needed: one place to make the growing pile of AI tools behave like an environment instead of a junk drawer.

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This article was originally published by DEV Community and written by Ryan Swift.

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