Kubeshark
MCP access to cluster-wide L4 and L7 network traffic, packets, APIs, and complete payloads.
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Overview
Kubeshark MCP (Model Context Protocol) server enables AI assistants like Claude Desktop, Cursor, and other MCP-compatible clients to query real-time Kubernetes network traffic. The MCP provides the tools — AI skills teach agents how to use them. Skills turn raw MCP capabilities into domain-specific workflows like root cause analysis, traffic filtering, and forensic investigation. See the skills README for installation and usage. - L7 API Traffic Analysis: Query HTTP, gRPC, Redis, Kafka, DNS transactions - L4 Network Flows: View TCP/UDP flows with traffic statistics - Cluster Management: Start/stop Kubeshark deployments (with safety controls) - PCAP Snapshots: Create and export network captures - Built-in Prompts: Pre-configured prompts for common analysis tasks
Installation
Installation instructions are not available yet.
Configuration
{
"mcpServers": {
"kubeshark": {
"command": "kubeshark",
"args": ["mcp"]
}
}Capabilities
- Query L7 API transactions with KFL filters
use `list_api_calls` to find HTTP, gRPC, Redis, Kafka, or DNS requests matching conditions like status codes or paths.
- Inspect a specific API call in detail
retrieve full request/response data for a single transaction with `get_api_call`.
- Get aggregated API statistics
summarize traffic patterns, error rates, or latency distributions using `get_api_stats`.
- View L4 network flows and summaries
list TCP/UDP connections and get connectivity overviews via `list_l4_flows` and `get_l4_flow_summary`.
- Create and manage PCAP snapshots
capture network traffic for offline analysis with `create_snapshot` and `list_snapshots`.
- Control L7 protocol dissection
enable or disable deep protocol parsing on demand using `enable_dissection` and `disable_dissection`.
Tags
- kubernetes
- network-automation
- monitoring
- devops-infrastructure
- security
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