Loading ToolBench
Autonomous knowledge acquisition from technical videos and talks. Provides tools for YouTube transcript fetching, cleaning, concept extraction, methodology identification, speaker analysis, and knowledge storage for AGI learning systems.
The server defines 6 tools with explicit schemas and descriptions visible in the source code. Tool naming follows verb_noun convention consistently (fetch_, clean_, extract_, analyze_, store_). However, descriptions are somewhat generic and lack LLM-optimization guidance. Input schemas are present but lack comprehensive constraints, no enums where appropriate, limited parameter descriptions, and no documented output schemas. Error handling is not evident in the provided code excerpt. The 'store_video_knowledge' tool accepts an 'object' type for video_metadata without a detailed schema, which reduces clarity. Overall, this is a competent but incomplete implementation that falls into the 'Fair' range.
Identify and separate multiple speakers in transcript (if available in source data).
Clean and structure transcript text. Removes repetition, formatting artifacts, and stutters.
Extract key technical concepts, terms, and topics discussed in video. Uses pattern matching and frequency analysis.
Extract techniques, methods, and approaches described in video. Identifies how-to content and best practices.
Fetch transcript from YouTube video using yt-dlp. Returns cleaned, structured transcript text.
Store extracted video knowledge in enhanced-memory for AGI learning. Creates structured memory entities.
Output schemas are not documented. No tool description specifies what fields are returned, their types, or data structure. LLMs cannot plan downstream calls or extract relevant data without documented output.
Parameter constraints are missing or underspecified. 'min_frequency' is an integer with no min/max bounds (could be negative or extremely large). 'focus_domains' is an array with no size limits. No enums declared where appropriate (e.g., language codes could be constrained to valid ISO-639-1 codes).
'store_video_knowledge' accepts 'video_metadata' as type 'object' with only a brief description. No nested schema provided. LLMs cannot validate what fields are required or their types. This violates the principle of explicit, machine-parseable schemas.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 61 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 0 | - | v1 |
Tool descriptions lack actionable guidance on WHEN to use each tool vs. similar alternatives. For example, 'extract_concepts' vs 'extract_methodologies' distinction is not clearly explained. Descriptions should state prerequisites, dependencies, and when to call which tool.
No error handling guidance visible in tool definitions or descriptions. No recovery hints for failure cases (e.g., 'If fetch fails, check URL validity'; 'If extraction yields no concepts, try lowering min_frequency'). Errors should categorize as retryable, user-fixable, or fatal.
Parameter descriptions are brief (many under 50 characters) and lack format specifications. E.g., 'language' description does not specify format (ISO-639-1 vs BCP-47) or list valid values. LLMs cannot distinguish between 'en', 'en-US', 'english' without constraints.