Loading ToolBench
A Model Context Protocol (MCP) server for Kubernetes with 270+ tools, 8 resources, and 8 prompts for AI assistants
This server exhibits severe definition quality gaps across all 10 tools. While tool names follow a reasonable verb_noun pattern (k8s-pods, k8s-logs, k8s-deploy), the evaluation reveals critical failures: (1) Tool descriptions are present but many are aspirational and do not match the actual implementation visible in source code, references to features like 'syntax highlighting', 'search', 'filtering by level', '3D interactive topology' cannot be verified in the provided Python source. (2) Input schemas are visible for all tools, but are minimal and lack richness: parameters like 'namespace', 'context', 'pod' have basic type definitions but many lack enum constraints, format specifications, or validation guidance. (3) Output schemas are entirely undocumented, no tool provides a description of what fields will be returned, breaking the agent's ability to chain tools or extract downstream IDs. (4) Error handling is not evident in any tool definition, violating the critical pattern that errors must guide recovery. (5) The tools lack composition structure, e.g., k8s-proxy appears to be a meta-tool that can invoke any other tool, which violates the single-responsibility principle and creates a tool-naming explosion risk. (6) Security concerns around credentials and secret injection are not addressed in the visible definitions. Most critically, source code inspection reveals only a Dockerfile and package.json files, the actual Python server implementation (src/server.ts referenced in tool definitions is a TypeScript path, not Python) is not provided, making detailed verification impossible.
Real-time 3D interactive Kubernetes cluster topology viewer with resource relationships, traffic visualization, and click-to-inspect details
Cluster health summary with node status, resource allocation, namespace quotas, and storage overview
Resource utilization analysis with waste detection, right-sizing recommendations, and savings calculator
Deployment management with rollout status, scaling, restart, rollback, and revision history
Event timeline visualization with type filtering, resource grouping, and time range selection
Helm release management with upgrade, rollback, uninstall, values diff, and release notes
No output schemas documented for any tool. Tools do not declare what fields will be returned, breaking downstream tool chaining and forcing agents to parse unstructured responses.
Descriptions claim advanced features (syntax highlighting, 3D visualization, search, filtering) that cannot be verified in source code. Aspirational descriptions mislead agents about actual capability.
k8s-proxy is a meta-tool that proxies calls to other tools. This violates single-responsibility principle and creates confusion about when to use it vs. calling tools directly. It also enables potential infinite recursion or security bypasses.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 47 | <=2025-11-25 | v2 |
| 2026-03-09 | C | 63 | - | v1 |
Real-time log viewer with syntax highlighting, search, filtering by level, and multi-container support
Network topology graph showing Services, Pods, Ingress connections with click-to-inspect details
Interactive pod viewer with filtering, sorting, status indicators, and quick actions (logs, delete, exec)
Proxy any kubectl-mcp-server tool call
Input parameters lack enum constraints, format specifications, and validation guidance. 'context' is an open string with no description of valid values. 'tail' (k8s-logs) has no min/max bounds. Agents will hallucinate invalid values.
No error handling guidance visible in any tool definition. Tools do not indicate whether failures are retryable, what the LLM should do next, or what error codes to expect.
Destructive/write operations (k8s-deploy, k8s-helm, k8s-proxy) lack confirmation or dry-run capability. Agents can trigger irreversible changes without safeguards.
Source code provided contains only Dockerfile and package.json. Actual Python server implementation (referenced as src/server.ts, a TypeScript path) is not available for inspection. Cannot verify tool registration, error handling, or actual schema enforcement.
No security model visible: credentials handling, permission gates, scope declarations, and audit trails are not documented in tool definitions. Kubernetes access control is a critical concern.
Parameter 'context' (present in 8 tools) is ambiguous, no description clarifies whether it is a kubeconfig context name, a file path, or something else. Forces agents to guess.
k8s-logs tool accepts 'follow: boolean' for real-time streaming, but no output schema specifies how streaming results are returned. Is it a list of log lines? A stream object? Agents cannot plan how to consume the response.