Multi-source content intelligent processor that automatically identifies input type, uploads to NotebookLM and generates specified formats. Supports deep analysis mode with three-round progressive questioning (overview → deep exploration → synthesis reflection).
Both tools have basic descriptions but lack critical schema documentation. Input parameters are visible in code but not formally declared with JSON Schema types/constraints. Descriptions are present but generic (under 200 chars). No output schemas documented. No error handling guidance. Parameter descriptions exist but lack format/constraint details. Tool names follow verb_noun pattern (positive), but overall quality is well below production baseline.
获取飞书文档的基本信息(不下载内容)
读取飞书文档内容并转换为 Markdown(支持图片)
No formal JSON Schema for tool inputs. Parameters visible in Python code but not declared as structured JSON Schema with type, required, enum, or pattern constraints. LLMs cannot validate inputs or understand constraints.
Output schemas not documented. Both tools return dicts with fields like 'success', 'title', 'content', 'error' but no formal schema definition. LLMs cannot plan downstream operations or extract fields reliably.
Parameter descriptions lack format/constraint details. 'url' param says 'Feishu document URL' but does not specify exact format, valid domain patterns, or what happens if invalid. 'cookies_str' lacks format specification (semicolon-separated key=value pairs mentioned in description but not formalized).
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 49 | <=2025-11-25 | v2 |
Error handling provides no recovery guidance. Errors return dict with 'success': false and 'error' string, but do not categorize as retryable/user-fixable/fatal or suggest next steps. LLM cannot decide whether to retry, ask user, or abort.
Tool descriptions are generic and under 100 chars. 'read_feishu_doc' description (89 chars) lacks context on when to use vs 'get_doc_info', what formats are supported, or prerequisites. Baseline for A+ tools is 194 chars average.