A lightweight Agent framework with tool registration and OpenAI LLM integration
This server has fundamental definition quality gaps. Tool descriptions are present but minimal (10-30 chars), falling well below the 194-char baseline for production tools. Parameter descriptions exist but are sparse and lack constraint details (no enums, ranges, or format specs). Input schemas are inferred from function signatures rather than explicitly defined, making them fragile. No output schemas are documented. Error handling is absent, no recovery guidance, no categorization. The tool registry auto-generates schemas from Python type hints, which is brittle and incomplete. No tool annotations (readOnlyHint/destructiveHint) despite having WRITE-risk tools (multi_edit, apply_patch). Overall, this reads as an early-stage framework, not production-ready.
应用 unified diff 格式的补丁到文件
执行基本数学运算(加法、减法、乘法、除法)
获取指定城市的天气信息
对单个文件进行多次原子性编辑操作
读取文件内容
在网络上搜索信息
Descriptions are critically short (10-30 chars). Rubric baseline is 194 chars. LLMs cannot determine when/why to select tools with minimal descriptions.
No output schemas documented. LLMs cannot plan downstream tool calls or extract required fields without knowing response structure.
WRITE-risk tools (multi_edit, apply_patch) lack destructiveHint annotations and no confirmation/dry-run pattern. Agents cannot distinguish safe from irreversible operations.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 38 | 2026-07-28+ | v2 |
Parameter descriptions lack constraint details: no enums, ranges, format specs, or validation rules. E.g., 'operation' in calculator should list valid values (add, subtract, multiply, divide) as an enum.
No error handling guidance. Tools do not document what errors can occur, whether they are retryable, or what the LLM should do next.