Why
Most AI agents fail in enterprise ops not because the models are weak, but because the tools the agents are given are weak — generic API wrappers with no operational taxonomy, no result shaping, no chaining model.
I designed the MCP tooling layer as a first-class engineering artifact: 200+ diagnostic tools, organized hierarchically by domain, each with clean schemas, deterministic behavior, and rich return types built for downstream correlation.
What's inside
- Multi-domain coverage — routing, BGP, WAN topology, customer onboarding, hardware health, SLA, incidents.
- Structured tool hierarchy — agents discover and reason about capabilities the same way an engineer does.
- Cross-cluster intelligence pipelines — a single tool call fans out across multiple database clusters in parallel.
- Composable troubleshooting workflows — agents chain primitives into full investigations.
Lesson
The most underrated AI-engineering work right now is tool design. Models get cheaper and smarter every quarter — well-designed tools compound forever.