Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-ingero-io-ingero": {
"args": [
"mcp-client-for-ollama"
],
"command": "uvx"
}
}
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uvx 'mcp-client-for-ollama' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
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Version: 0.19.0
The only GPU observability tool your AI assistant can talk to.
"What caused the GPU stall?" → "forward() at train.py:142 - cudaMalloc spiking 48ms during CPU contention. 9,829 calls, 847 scheduler preemptions."
Ingero is a production-grade eBPF agent that traces the full chain - from Linux kernel events through CUDA API calls to your Python source lines - with <2% overhead, zero code changes, and one binary.
# Install (Linux amd64; arm64 / Docker / source variants are below)
# ingero-version:install-curl product=ingero channel=stable
VERSION=0.19.0
curl -fsSL "https://github.com/ingero-io/ingero/releases/download/v${VERSION}/ingero_${VERSION}_linux_amd64.tar.gz" | tar xz
sudo mv ingero /usr/local/bin/
# Trace your GPU workload
sudo ingero trace
# Explain what happened (no sudo)
ingero explain --since 5m
No GPU? ingero demo --no-gpu incident runs the full causal-chain diagnosis on
synthetic data; no root, no GPU, no ceremony. To poke the dashboards and the
Echo query API entirely in your browser, open the
zero-hardware cloud demo
in a GitHub Codespace.
After that:
docs/ml_eng_sample_investigation_session.md.docs/quickstart_fleet.md.