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A standalone AI agent platform with multi-LLM support, MCP server integration, and agent management capabilities
Two tools with basic schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. list_tools has reasonable parameter descriptions (category enum, is_enabled, skip, limit), but get_tool lacks parameter description. Neither tool documents return structure or error handling. Descriptions are minimal (10-20 chars) and lack context on when to use each tool. No tool annotations (readOnlyHint, idempotentHint). Output schemas inferred from Pydantic models (ToolRead, ToolList) but not explicitly documented in tool definitions. Error handling present in code (404 for missing tool) but not surfaced in tool descriptions. Composition is sound (two separate read-only tools), but parameter naming could be clearer (tool_id vs tool_name acceptance).
Get a tool by ID.
List all available tools.
get_tool parameter 'tool_id' lacks description. LLMs cannot infer whether to pass UUID, name, or display_name.
Tool descriptions are under 20 characters ('List all available tools.' = 27 chars, 'Get a tool by ID.' = 16 chars). Too brief to guide LLM selection or explain when to use each tool vs alternatives.
Output schemas (ToolRead, ToolList) are not documented in tool definitions. LLMs cannot plan downstream calls or extract required fields without seeing return structure.
No tool annotations (readOnlyHint, idempotentHint, destructiveHint). Both tools are read-only and idempotent but this is not declared, forcing LLMs to infer safety properties.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 55 | 2026-07-28+ | v2 |
Error handling (404 for missing tool) exists in code but is not documented in tool descriptions. LLMs cannot anticipate failure modes or recovery steps.