MCP server
Real-time Kubernetes network traffic visibility and API analysis for HTTP, gRPC, Redis, Kafka, DNS.
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MCP tools
No tool declarations could be read from the package source. They show once the server is installed.
Public scan report
scanner v0.1.9 · 2026-09-23 · same rubric, same numbers if you re-run it
- –Code scanpackage could not be scannedn/a
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 0 days ago15/15
- Maintainer identityregistry namespace matches repository owner; GitHub account older than a year9/10
What the publisher says
From the MCP repository's README, as published. We do not edit it. Read it on GitHub
Network Observability for SREs & AI Agents
Live Demo · Docs
Kubeshark indexes cluster-wide network traffic at the kernel level using eBPF — delivering instant answers to any query using network, API, and Kubernetes semantics.
What you can do:
- Download Retrospective PCAPs — cluster-wide packet captures filtered by nodes, time, workloads, and IPs. Store PCAPs for long-term retention and later investigation.
- Visualize Network Data — explore traffic matching queries with API, Kubernetes, or network semantics through a real-time dashboard.
- See Encrypted Traffic in Plain Text — automatically decrypt TLS/mTLS traffic using eBPF, with no key management or sidecars required.
- Integrate with AI — connect your favorite AI assistant (e.g. Claude, Copilot) to include network data in AI-driven workflows like incident response and root cause analysis.
Get Started
helm repo add kubeshark https://helm.kubeshark.com
helm install kubeshark kubeshark/kubeshark
kubectl port-forward svc/kubeshark-front 8899:80Open http://localhost:8899 in your browser. You're capturing traffic.
For production use, we recommend using an ingress controller instead of port-forward.
Connect an AI agent via MCP:
brew install kubeshark
claude mcp add kubeshark -- kubeshark mcpMCP setup guide →
Network Data for AI Agents
Kubeshark exposes cluster-wide network data via MCP — enabling AI agents to query traffic, investigate API calls, and perform root cause analysis through natural language.
"Why did checkout fail at 2:15 PM?"
"Which services have error rates above 1%?"
"Show TCP retransmission rates across all node-to-node paths"
"Trace request abc123 through all services"
Works with Claude Code, Cursor, and any MCP-compatible AI.
MCP setup guide →
AI Skills
Open-source, reusable skills that teach AI agents domain-specific workflows on top of Kubeshark's MCP tools:
Install as a Claude Code plugin:
/plugin marketplace add kubeshark/kubeshark
/plugin install kubesharkOr clone and use directly — skills trigger automatically based on conversation context.
AI Skills docs →
Query with API, Kubernetes, and Network Semantics
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
Install directly
The registry entry has no remote endpoint or installable package.
MCP: common questions
- Is MCP server safe?
- Yes, by our scan: it is graded A (90/100). Read the MCP safety report
- Does MCP need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is MCP maintained?
- The last commit was 2 days ago (2026-09-23). The latest release is v53.4.0.
- What can I use instead of MCP?
- Servers from other publishers that do the same job: Infrawise MCP server, Myc MCP server and Redis MCP Server. Compare all MCP alternatives.
Alternatives to MCP
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