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Kubeshark

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kubeshark

eBPF-powered network observability for Kubernetes. Indexes L4/L7 traffic with full K8s context, decrypts TLS without keys. Queryable by AI agents via MCP and humans via dashboard.

Publisherkubeshark
Repositorykubeshark
LanguageGo
Forks
549
Stars
12.1K
Available tools
0
Transport typestdio
Categories
LicenseApache-2.0
Links
  • Connect tools to AI workflows

    Kubeshark exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    12.1K stars and 549 forks from the linked repository.


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.

Kubeshark


Get Started

bash
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:

bash
brew install kubeshark
claude mcp add kubeshark -- kubeshark mcp

MCP 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 Demo

MCP setup guide →

AI Skills

Open-source, reusable skills that teach AI agents domain-specific workflows on top of Kubeshark's MCP tools:

SkillDescription
Network RCARetrospective root cause analysis — snapshots, dissection, PCAP extraction, trend comparison
KFLKFL (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.

AI Skills docs →


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 query combining API, Kubernetes, and network semantics

KFL reference → · Traffic indexing →

Workload Dependency Map

A visual map of how workloads communicate, showing dependencies, traffic volume, and protocol usage across the cluster.

Service Map

Learn more →

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.

Traffic Retention

Snapshots guide → · Cloud storage →


Features

FeatureDescription
Traffic SnapshotsPoint-in-time snapshots with cloud storage (S3, Azure Blob, GCS), PCAP export for Wireshark
Traffic IndexingReal-time and delayed L7 indexing with request/response matching and full payloads
Protocol SupportHTTP, gRPC, GraphQL, Redis, Kafka, DNS, and more
TLS DecryptioneBPF-based decryption without key management, included in snapshots
AI IntegrationMCP server + open-source AI skills for network RCA and traffic filtering
KFL Query LanguageCEL-based query language with Kubernetes, API, and network semantics
100% On-PremisesAir-gapped support, no external dependencies

Install

MethodCommand
Helmhelm repo add kubeshark https://helm.kubeshark.com && helm install kubeshark kubeshark/kubeshark
Homebrewbrew install kubeshark && kubeshark tap
BinaryDownload

Installation guide →


Contributing

We welcome contributions. See CONTRIBUTING.md.

License

Apache-2.0

Use Kubeshark MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Kubeshark is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Kubeshark as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Kubeshark MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "kubeshark-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "<mcp-server-package>"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Kubeshark MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Kubeshark MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Kubeshark to help me with this task?
Kubeshark
Sure. I read it.
Here is what I found using Kubeshark.

Frequently asked questions

What is the Kubeshark MCP server used for?

Kubeshark is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Kubeshark MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Kubeshark with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Kubeshark MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Kubeshark connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Kubeshark MCP to TypingMind?

Kubeshark runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Kubeshark MCP provide in TypingMind?

Kubeshark exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Kubeshark MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Kubeshark requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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