Ingero
eBPF-based GPU causal observability agent with MCP server. Traces CUDA Runtime and Driver APIs via kernel uprobes and host events via tracepoints to build causal chains explaining GPU latency. 7 tools: get_check, get_trace_stats, get_causal_chains, g
What is Ingero?
Ingero is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to ebpf-based gpu causal observability agent with mcp server. traces cuda runtime and driver apis via kernel uprobes and host events via tracepoints to build causal chains explaining gpu latency. 7 tools...
eBPF-based GPU causal observability agent with MCP server. Traces CUDA Runtime and Driver APIs via kernel uprobes and host events via tracepoints to build causal chains explaining GPU latency. 7 tools: get_check, get_trace_stats, get_causal_chains, g
This server falls under the Monitoring & Observability category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- eBPF-based GPU causal observability agent with MCP server. T
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx ingeroConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Ingero
Ingero is an eBPF-based GPU causal observability agent with an integrated MCP server that traces CUDA Runtime and Driver API calls via kernel uprobes and host events via tracepoints to build causal chains explaining GPU latency. It exposes seven MCP tools — including get_check, get_trace_stats, get_causal_chains, and others — enabling AI assistants to query live GPU performance data and diagnose latency bottlenecks in CUDA workloads running on Kubernetes or bare-metal hosts. This is designed for ML engineers, platform engineers, and SRE teams investigating GPU performance issues and incident response.
Prerequisites
- Linux host with eBPF support (kernel 5.8+ recommended) and CUDA-capable GPU
- CUDA Runtime installed on the target host where GPU workloads run
- Root or CAP_BPF + CAP_PERFMON privileges to load eBPF programs
- Kubernetes cluster (optional) if tracing containerized GPU workloads
- An MCP-compatible AI client such as Claude Desktop or Claude Code
Review system requirements
Ingero uses eBPF uprobes to trace CUDA APIs. Verify your Linux kernel version and that the CUDA runtime is installed on the target machine.
uname -r
nvcc --versionClone the Ingero repository
Clone the repository from GitHub to obtain the source code and deployment manifests.
git clone https://github.com/ingero-io/ingero.git
cd ingeroDeploy the Ingero agent
Follow the deployment instructions in the repository. For Kubernetes, apply the provided manifests. The agent requires privileged access to load eBPF programs.
# For Kubernetes:
kubectl apply -f deploy/Start the MCP server
Once the agent is running and tracing CUDA calls, start the MCP server component to expose the seven observability tools to your AI client.
Configure your AI client to connect
Add the Ingero MCP server to your AI client configuration to enable GPU observability queries.
Ingero Examples
Client configuration
Connect an MCP client to the Ingero agent. Adjust the command and host details based on your deployment.
{
"mcpServers": {
"ingero": {
"command": "npx",
"args": ["ingero"]
}
}
}Prompts to try
Use these prompts to investigate GPU performance issues via the Ingero MCP tools.
- "Check the current GPU health status using the ingero MCP."
- "Get trace statistics for the last 10 minutes of GPU activity."
- "Show me the causal chains explaining the GPU latency spike at 14:32."
- "Identify which CUDA kernel calls are contributing most to inference latency."
- "Is there a CUDA graph bottleneck in the current workload?"Troubleshooting Ingero
eBPF program fails to load with permission denied
The Ingero agent requires elevated privileges to load eBPF programs. Ensure the process has CAP_BPF and CAP_PERFMON capabilities, or run as root. In Kubernetes, the DaemonSet must run with privileged: true in its security context.
No trace data is collected despite the agent running
Verify that CUDA workloads are actually running on the target host. The agent traces CUDA Runtime and Driver API calls via uprobes — if no CUDA processes are active, trace stats will be empty. Confirm the CUDA library path is correctly detected by the agent.
MCP server is unreachable from the AI client
Confirm the Ingero MCP server process is running and listening on the expected port. Check firewall rules if connecting across hosts. For Kubernetes deployments, ensure the MCP service is exposed via a NodePort or LoadBalancer service.
Frequently Asked Questions about Ingero
What is Ingero?
Ingero is a Model Context Protocol (MCP) server that ebpf-based gpu causal observability agent with mcp server. traces cuda runtime and driver apis via kernel uprobes and host events via tracepoints to build causal chains explaining gpu latency. 7 tools: get_check, get_trace_stats, get_causal_chains, g It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Ingero?
Follow the installation instructions on the Ingero GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Ingero?
Ingero works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Ingero free to use?
Yes, Ingero is open source and available under the Apache-2.0 license. You can use it freely in both personal and commercial projects.
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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