Inspektor Gadget MCP server
MCP ServerFree** - Debug your Container and Kubernetes workloads with an AI interface powered by eBPF.
Capabilities12 decomposed
ebpf-based kernel observability tool exposure via mcp protocol
Medium confidenceExposes Inspektor Gadget's eBPF-based kernel observability tools as MCP (Model Context Protocol) tools that LLMs can invoke. The server implements a four-layer architecture translating LLM tool calls into gadget executions by maintaining a GadgetToolRegistry that dynamically registers tools, manages their lifecycle, and returns structured telemetry data. This enables AI agents to autonomously select and execute low-level system diagnostics without requiring direct kernel access or eBPF knowledge.
Bridges kernel-level eBPF observability directly into LLM tool calling via MCP protocol, eliminating the need for LLMs to understand eBPF or shell commands. Uses a four-layer architecture (MCP transport → tool registry → gadget manager → eBPF execution) with dynamic tool discovery from Artifact Hub, enabling AI agents to discover and invoke new observability tools without server restart.
Provides kernel-level observability to LLMs without requiring shell access or manual command construction, unlike traditional SSH-based debugging or kubectl exec workflows that require explicit user prompting.
dynamic gadget discovery and registration from multiple sources
Medium confidenceImplements a pluggable discovery system (Discoverer interface with ArtifactHubDiscoverer and BuiltinDiscoverer implementations) that automatically discovers available eBPF gadgets from Artifact Hub and built-in sources, then registers them as MCP tools with schema validation. The GadgetToolRegistry maintains a cache of gadget metadata (GadgetInfo) to avoid repeated discovery overhead, enabling the server to expose new gadgets without code changes or restarts.
Implements a two-tier discovery system combining Artifact Hub (community-driven, extensible) with built-in gadgets (reliable, offline-capable), using a pluggable Discoverer interface that allows custom discovery backends. Caches gadget metadata in GadgetInfo structures to decouple discovery latency from tool invocation frequency.
Enables dynamic gadget discovery without requiring manual tool registration or server configuration changes, unlike static tool registries in traditional MCP servers or Kubernetes operators that require CRD updates.
gadget execution timeout and resource constraint management
Medium confidenceImplements configurable timeout management for gadget execution, preventing long-running or hung gadgets from blocking the LLM indefinitely. Timeouts are specified per gadget (via RunOptions) and enforced at the process level using context cancellation and signal handling. Resource constraints (memory, CPU) can be configured via environment variables or command-line flags, with defaults tuned for typical observability workloads.
Implements context-based timeout enforcement with configurable per-gadget timeouts and resource constraints, preventing hung gadgets from blocking the LLM. Timeout values are discoverable via tool schemas, allowing LLMs to understand expected execution times.
Provides bounded gadget execution with configurable timeouts, whereas unbounded tool execution in traditional LLM agents can cause indefinite blocking and resource exhaustion.
gadget output capture and result formatting for llm consumption
Medium confidenceCaptures gadget stdout/stderr output, parses it into structured formats (JSON, CSV, or text), and formats it for LLM consumption. The output capture system handles large outputs by truncating or sampling data to fit LLM context windows, preserves structured data formats for programmatic analysis, and includes execution metadata (duration, exit code, resource usage). Output is returned as part of the MCP tool result, enabling the LLM to analyze gadget results directly.
Implements intelligent output capture with context-aware truncation and structured formatting, preserving gadget output in LLM-friendly formats while respecting context window constraints. Includes execution metadata to provide execution context to the LLM.
Provides structured, context-aware output formatting for LLM consumption, whereas raw gadget output requires the LLM to parse unstructured text and manually extract relevant information.
gadget lifecycle management and execution orchestration
Medium confidenceThe GadgetManager component manages the complete lifecycle of gadget execution: parsing tool call parameters, validating inputs against gadget schemas, spawning gadget processes (via RunOptions), capturing structured output, and returning results to the LLM. It handles both synchronous execution (blocking until gadget completes) and asynchronous patterns, with support for timeout management, resource cleanup, and error propagation from kernel-level failures.
Implements a state machine-based gadget lifecycle (parse → validate → execute → capture → return) with explicit error handling at each stage, using RunOptions to encapsulate execution context and timeout management. Decouples gadget discovery from execution, allowing the LLM to query available gadgets independently of execution readiness.
Provides structured error propagation and timeout management for kernel-level tools, whereas direct kubectl exec or SSH-based debugging requires manual error parsing and timeout handling in the LLM prompt.
kubernetes-aware gadget targeting and rbac enforcement
Medium confidenceIntegrates with Kubernetes API (via kubeconfig) to resolve pod/container targets, validate RBAC permissions, and enforce ServiceAccount-based access control when running in-cluster. The server supports three deployment modes (binary, Docker, Kubernetes in-cluster) with environment-specific authentication: local kubeconfig for binary/Docker, ServiceAccount RBAC for in-cluster deployments. Tool execution is scoped to the authenticated user's permissions, preventing unauthorized access to pods or namespaces.
Implements three distinct deployment modes (binary, Docker, in-cluster) with environment-specific authentication and RBAC enforcement, using Kubernetes API for pod resolution and permission validation. RBAC is enforced at the ServiceAccount level in in-cluster deployments, preventing unauthorized gadget execution without requiring additional authentication layers.
Provides Kubernetes-native RBAC enforcement for observability access, whereas traditional SSH-based debugging or kubectl exec requires manual permission management and does not integrate with Kubernetes RBAC policies.
mcp protocol server implementation with stdio and http transport
Medium confidenceImplements the Model Context Protocol (MCP) server specification using the mcp-go library, supporting both stdio (for local IDE integration) and HTTP/SSE transports (for remote access). The server exposes gadgets as MCP tools with JSON schemas, handles tool call requests from LLM clients, and returns structured results. Transport selection is automatic based on deployment context: stdio for binary/Docker, HTTP for Kubernetes in-cluster.
Implements MCP server using mcp-go library with dual transport support (stdio for local, HTTP/SSE for remote), automatically selecting transport based on deployment context. Exposes gadgets as MCP tools with JSON schemas, enabling LLM clients to discover and invoke tools without custom integration code.
Provides a standard MCP interface compatible with multiple LLM clients (Copilot, Claude, custom agents), whereas custom REST APIs or gRPC services require client-specific integration and lack standardized tool discovery.
structured data enrichment and telemetry formatting
Medium confidenceImplements a data enrichment pipeline that transforms raw eBPF output into structured, LLM-friendly formats. The pipeline parses gadget output (text, JSON, CSV), enriches it with contextual metadata (pod name, namespace, timestamp), and formats it for LLM consumption. This includes converting kernel-level syscall traces into human-readable summaries, aggregating network packet data into flow statistics, and correlating events across multiple gadgets.
Implements a gadget-aware enrichment pipeline that transforms raw eBPF output into LLM-friendly structured data, correlating metadata from Kubernetes API with kernel-level telemetry. Enrichment is pluggable per gadget type, allowing custom gadgets to define their own enrichment logic.
Provides LLM-optimized telemetry formatting with Kubernetes context, whereas raw eBPF output requires the LLM to parse unstructured text and manually correlate with cluster metadata.
ephemeral and lifecycle-managed gadget tool registration
Medium confidenceSupports two gadget tool registration patterns: default gadgets (statically registered at startup) and ephemeral gadgets (dynamically registered/unregistered based on cluster state). Ephemeral gadgets are registered when their prerequisites are met (e.g., a specific Kubernetes API version is available) and unregistered when prerequisites are no longer satisfied. This enables the server to adapt its tool set to the current cluster capabilities without restart.
Implements a two-tier gadget registration system (default + ephemeral) with prerequisite-based lifecycle management, enabling the server to adapt its tool set to cluster capabilities without restart. Ephemeral gadgets are registered/unregistered based on health checks, providing graceful degradation when cluster capabilities change.
Provides automatic capability detection and tool adaptation, whereas static tool registries require manual configuration updates or server restarts when cluster capabilities change.
multi-deployment environment adaptation and configuration
Medium confidenceSupports three distinct deployment modes (binary executable, Docker container, Kubernetes in-cluster) with environment-specific configuration and initialization. The server detects its deployment context at startup and adapts authentication (kubeconfig vs ServiceAccount), transport (stdio vs HTTP), and resource constraints accordingly. Configuration is driven by command-line flags, environment variables, and mounted files, with sensible defaults for each deployment mode.
Implements environment-aware initialization that detects deployment context (binary/Docker/Kubernetes) and automatically configures authentication, transport, and resource constraints. Configuration is driven by flags, environment variables, and mounted files with deployment-specific defaults, eliminating manual configuration for common deployment patterns.
Provides automatic environment detection and configuration, whereas traditional observability tools require manual configuration for each deployment mode or separate binaries for different environments.
tool schema validation and parameter constraint enforcement
Medium confidenceImplements JSON schema-based validation for gadget parameters, enforcing type constraints, required fields, allowed values, and parameter ranges before gadget execution. Schemas are defined per gadget and registered with the MCP server, enabling LLM clients to discover parameter constraints and the server to reject invalid tool calls early. Validation errors are returned to the LLM with detailed messages indicating which parameters are invalid and why.
Implements JSON schema-based parameter validation with detailed error messages, enabling early rejection of invalid tool calls and preventing wasted gadget executions. Schemas are discoverable by MCP clients, allowing LLMs to understand parameter constraints without trial-and-error.
Provides schema-driven parameter validation with LLM-discoverable constraints, whereas unvalidated tool APIs require the LLM to learn constraints through failed executions.
artifact hub integration for gadget discovery and versioning
Medium confidenceIntegrates with Artifact Hub (https://artifacthub.io) to discover published eBPF gadgets in OCI artifact format, including version information, compatibility metadata, and documentation. The ArtifactHubDiscoverer queries Artifact Hub API, caches gadget metadata locally, and enables the server to expose community-contributed gadgets alongside built-in tools. Gadget versioning is tracked, allowing the server to select compatible versions based on kernel/cluster capabilities.
Integrates with Artifact Hub for community-driven gadget discovery and versioning, enabling dynamic gadget catalog expansion without server code changes. Caches gadget metadata locally to reduce discovery latency and supports version selection based on compatibility metadata.
Provides community-driven gadget discovery and versioning, whereas static gadget registries require manual updates and do not support community contributions.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓DevOps engineers and SREs building AI-assisted debugging workflows for Kubernetes clusters
- ✓Platform teams integrating LLM-based observability into existing eBPF infrastructure
- ✓Developers using Claude Desktop or VS Code Copilot Chat who need kernel-level diagnostics
- ✓Teams maintaining a shared Inspektor Gadget ecosystem with evolving tool catalogs
- ✓Organizations publishing custom eBPF gadgets to Artifact Hub for AI-driven consumption
- ✓Operators who want to selectively enable/disable gadgets based on cluster policies
- ✓Production deployments where gadget execution must be bounded to prevent LLM client timeouts
- ✓Multi-tenant environments where resource constraints prevent one gadget from starving others
Known Limitations
- ⚠Requires kernel version 5.8+ with eBPF support; older kernels cannot execute gadgets
- ⚠Gadget execution latency depends on kernel scheduling and eBPF program complexity; some gadgets may take 5-30 seconds to produce results
- ⚠No built-in persistence of gadget results; requires external storage integration for audit trails
- ⚠Limited to Linux/Kubernetes environments; no Windows or macOS support
- ⚠Artifact Hub discovery requires network connectivity; offline deployments must use built-in gadgets only
- ⚠Gadget metadata caching is in-memory; cache is lost on server restart and must be rebuilt
Requirements
Input / Output
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** - Debug your Container and Kubernetes workloads with an AI interface powered by eBPF.
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