Ingero
Give your agent eBPF-backed GPU causal traces and seven debugging tools when training or inference jobs fail in production-like environments.
Overview
io.github.ingero-io/ingero is a MCP server for the Operate phase that provides eBPF GPU causal observability through seven tools for AI-assisted debugging.
What is this MCP server?
- eBPF-based GPU causal observability for production and staging clusters
- Seven MCP tools purpose-built for AI-assisted GPU debugging
- OCI image ghcr.io/ingero-io/ingero:v0.8.2 with stdio transport
- Causal tracing emphasis—link symptoms to upstream GPU events, not generic logs alone
- Seven MCP tools stated in server description
- Server version 0.8.2 OCI image ghcr.io/ingero-io/ingero:v0.8.2
- eBPF-based GPU causal observability per publisher description
What problem does it solve?
GPU outages and slowdowns are hard to diagnose with plain logs, leaving solo ML builders guessing while accelerators sit idle or burn budget.
Who is it for?
Indie AI builders running custom GPU workloads who want agent-driven incident analysis with eBPF depth.
Skip if: Pure CPU web apps with no GPU path, or beginners without container and cluster access to deploy the Ingero image.
What do I get? / Deliverables
Your agent can query causal GPU telemetry via MCP and narrow root causes faster during incidents and capacity investigations.
- Seven agent-invokable MCP tools for GPU causal debugging
- Structured observability queries instead of ad-hoc log grepping
- Faster incident narrowing for accelerator-bound AI products
Recommended MCP Servers
Journey fit
Operate is where GPU-heavy products either stay up or bleed money on silent slowdowns; causal observability is a production monitoring concern, not a landing-page task. Monitoring is the shelf for telemetry and root-cause tooling that explains why GPUs misbehave under real load.
How it compares
GPU causal observability MCP, not a workflow connector catalog or prompt-injection firewall.
Common Questions / FAQ
Who is Ingero for?
Solo and small-team ML engineers operating GPU training or inference who want MCP tools for causal debugging alongside their coding agent.
When should I use Ingero?
Use it in Operate when jobs regress in latency, fail intermittently, or need post-incident GPU root-cause analysis.
How do I add Ingero to my agent?
Configure the stdio MCP entry for ghcr.io/ingero-io/ingero:v0.8.2 per your client’s OCI MCP instructions and ensure GPU workloads are instrumented by Ingero.