@ingero-io
基本信息
eBPF-based GPU causal observability agent with MCP server. Traces CUDA Runtime/Driver APIs and host kernel events to build causal chains explaining GPU latency
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"ingero": {
"command": "ingero",
"args": [
"mcp"
],
"env": {}
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
Ingero is an eBPF-based agent that provides production-safe, kernel-level causal tracing for GPU workloads. It answers the question: "Why is my GPU training/inference slow right now?" It is designed for developers and operators debugging GPU performance issues.
Install the single Ingero binary, then start the MCP server with ingero mcp --db ~/.ingero/ingero.db for stdio transport (Claude Code, Cursor) or ingero mcp --db ~/.ingero/ingero.db --http :8090 for HTTPS. No code changes are required. Use the provided MCP tools to diagnose performance.
Ingero traces CUDA Runtime and Driver API calls, host kernel tracepoints, and system context from /proc. All event data is stored in a local SQLite database at the configured path (default `~/.ingero/ingero.db`) with a 10 GB rolling cap.
No code changes are needed. For production monitoring, root access is required to attach eBPF programs; however, the `run_demo` tool can be used without a GPU or root privileges.
Ingero is tested on A10, A100, H100, GH200, RTX 3090, and RTX 4090. It supports Kubernetes deployments via a DaemonSet and Helm chart, and it works with Python 3.10/3.11/3.12.
Ingero's MCP server supports stdio (local) and HTTP (HTTPS for remote clients). The README does not specify built-in authentication mechanisms.
Ingero provides causal chains that link system context and host-level events to specific CUDA calls, offering root cause analysis with severity ranking and fix recommendations, rather than just aggregate metrics or sampling profiles.
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