MCP server that provides tools to interact with CloudStack infrastructure management system by dynamically exposing all CloudStack API endpoints as callable tools
This server uses dynamic tool generation from CloudStack API responses. The critical issue is that tool definitions are not statically visible in the source code, tools are generated at runtime via CloudStackApiToJsonSchema function. While the code attempts to create schema from CloudStack API metadata, the actual tool names, descriptions, parameter schemas, and output formats cannot be verified from static analysis. The tool registration pattern is indirect and inferred rather than explicit. No actual tool definitions with names, descriptions, or verified schemas are visible in the provided source. The schema generation function exists but its output quality, parameter descriptions, and error handling capabilities are not documented or verifiable. This violates HARD SCORING RULE: 'If you cannot see the actual tool definition in the source (only inferred): cap that tool's overall at 50.' Additionally, the single 'dynamic CloudStack API names' entry represents an unknown number of actual tools with unknown quality characteristics.
Dynamic CloudStack API tool - all CloudStack API endpoints are dynamically exposed as tools
Tool definitions are dynamically generated at runtime, not statically defined. No explicit tool registration with verified schemas is visible in source code.
No input schemas visible for any tools. Schema generation is inferred from CloudStackApiToJsonSchema function, but actual parameter types, descriptions, and constraints cannot be verified.
Tool description is generic placeholder text ('Dynamically generated tools...'), not an actual actionable description. Does not explain when/why to use specific tools, what they return, or how they differ.
Dynamic tool names are CloudStack API names (e.g., likely 'listVirtualMachines', 'createUser', etc.). These are not human-friendly identifiers matching chat data model. CloudStack API verbs do not align with agent tool naming conventions (get_, create_, update_, delete_, search_, list_).
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 11 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 9 | - | v1 |
No documented output schemas. LLMs cannot plan downstream tool calls or extract relevant data without knowing what fields to expect from dynamically generated tools.
No visible error handling guidance. If a CloudStack API call fails, unclear whether errors are retryable, user-fixable, or fatal. No recovery hints provided to LLM.
Risk annotated as 'WRITE' but no confirmation pattern, dry-run support, or irreversibility warning visible. Destructive operations lack safeguards against agent mistakes.
No parameter descriptions visible for generated tools. CloudStack API parameters likely have opaque names (e.g., 'id', 'name', 'type') without context for LLM understanding.
Secrets/credentials handling not visible. No evidence of secret injection pattern. CloudStack API calls may require API keys/tokens, if exposed as tool parameters, they leak into agent traces.
Tool composition unclear. If a common workflow requires multiple CloudStack API calls (e.g., create VM → configure networking → add storage), these are fragmented across multiple tools with no wrapper. Wastes tokens and invites mid-chain failures.