About kubeshark
Kubeshark is a network observability tool for SREs and AI agents that indexes cluster-wide Kubernetes traffic at the kernel level using eBPF. It answers queries using network, API, and Kubernetes semantics, and exposes this data to AI assistants through MCP.
Key features
- 01Cluster-wide traffic indexing with eBPF
- 02Protocol parsing for HTTP, gRPC, Redis, Kafka, DNS
- 03TLS and mTLS decryption without sidecars or key management
- 04PCAP download filtered by node, time, workload, IP
- 05KFL query language across Kubernetes, API, network semantics
- 06Workload dependency map
Use cases
- 01Root cause analysis of failed requests in a cluster
- 02Retrospective traffic review and PCAP export for a time window
- 03Natural-language analysis of API calls and TCP retransmissions by an AI agent
MCP support
Kubeshark runs an MCP server that exposes cluster-wide network data to MCP clients, letting tools such as Claude Code and Cursor query traffic during investigations.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
Network Observability for SREs & AI Agents
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 8899:80
Open 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 mcp
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.
AI Skills
Open-source, reusable skills that teach AI agents domain-specific workflows on top of Kubeshark's MCP tools:
| Skill | Description |
|---|---|
| Network RCA | Retrospective root cause analysis — snapshots, dissection, PCAP extraction, trend comparison |
| KFL | KFL (Kubeshark Filter Language) expert — writes, debugs, and optimizes traffic filters |
Install as a Claude Code plugin:
/plugin marketplace add kubeshark/kubeshark
/plugin install kubeshark
Or clone and use directly — skills trigger automatically based on conversation context.
Query with API, Kubernetes, and Network Semantics
Kubeshark indexes cluster-wide network traffic by parsing it according to protocol specifications, with support for HTTP, gRPC, Redis, Kafka, DNS, and more. A single KFL query can combine all three semantic layers — Kubernetes identity, API context, and network attributes — to pinpoint exactly the traffic you need. No code instrumentation required.
KFL reference → · Traffic indexing →
Workload Dependency Map
A visual map of how workloads communicate, showing dependencies, traffic volume, and protocol usage across the cluster.
Traffic Retention & PCAP Export
Capture and retain raw network traffic cluster-wide, including decrypted TLS. Download PCAPs scoped by time range, nodes, workloads, and IPs — ready for Wireshark or any PCAP-compatible tool. Store snapshots in cloud storage (S3, Azure Blob, GCS) for long-term retention and cross-cluster sharing.
Snapshots guide → · Cloud storage →
Features
| Feature | Description |
|---|---|
| Traffic Snapshots | Point-in-time snapshots with cloud storage (S3, Azure Blob, GCS), PCAP export for Wireshark |
| Traffic Indexing | Real-time and delayed L7 indexing with request/response matching and full payloads |
| Protocol Support | HTTP, gRPC, GraphQL, Redis, Kafka, DNS, and more |
| TLS Decryption | eBPF-based decryption without key management, included in snapshots |
| AI Integration | MCP server + open-source AI skills for network RCA and traffic filtering |
| KFL Query Language | CEL-based query language with Kubernetes, API, and network semantics |
| 100% On-Premises | Air-gapped support, no external dependencies |
Install
| Method | Command |
|---|---|
| Helm | helm repo add kubeshark https://helm.kubeshark.com && helm install kubeshark kubeshark/kubeshark |
| Homebrew | brew install kubeshark && kubeshark tap |
| Binary | Download |
Contributing
We welcome contributions. See CONTRIBUTING.md.
License
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.