Building and orchestrating multiple AI agents shouldn't require endless configuration files, fragile tool routing, or babysitting a terminal just to click "approve." If you are building LLM applications, you already know the friction of manually configuring tool calling, integrating knowledge bases, and setting up web search from scratch for every single agent. We built MCP ADMIN to turn that weeks-long engineering slog into a seamless, centrally managed workflow
We experienced the exact pain of scaling agentic workflows. Writing custom rules to control agent behavior and staring at dashboards to manually approve simple API calls was draining our engineering velocity. We realized the ecosystem needed a dedicated control plane—not just to connect tools, but to govern how and when agents are allowed to use them.
We have abstracted away the tedious plumbing of agent orchestration so you can focus entirely on building smarter intelligence. Dive in, connect your models, and let us know what you think in the comments!