Loading ToolBench
ML research skills and utilities for paper discovery, grounding verification, and dataset inspection
Server has 12 tools with reasonable naming (verb-noun pattern) and descriptions (avg ~150 chars), but critical gaps undermine quality. Input schemas are present but lack proper JSON Schema structure with explicit type definitions for all parameters. Many parameter descriptions are minimal (under 50 chars). No output schemas documented. Error handling is absent, tools fail silently or return raw shell output. No tool annotations (readOnlyHint/destructiveHint). Tools like download_paper and paper_text perform writes but lack confirmation patterns. Parameter relationships (e.g., format enum in download_paper) are documented but validation is implicit. Overall: functional but below production baseline.
Check that model-specific identifiers in code actually appear in that model's provenance archive. Verifies Hub repo ids and model symbols against archived source.
Search papers via HF Papers or Semantic Scholar, or walk the citation graph with influence flags and intents. Supports filtered search, citation graph traversal, and paper metadata retrieval.
Fetch arXiv PDF and/or ar5iv HTML to a local archive. Supports batch processing and skip-if-exists caching.
Pull HF Papers metadata including linked Hub models, datasets, and Spaces for a given arXiv paper.
Retrieve schema and sample rows for a Hugging Face dataset via REST endpoints. No Python dependencies required.
Resolve a model name to its provenance archive and map its contents. Shows grounded facts available including checkpoint ids, notes, source code, and papers.
Deduplicate and count overlaps in JSONL paper records from multiple crawl_arxiv.sh or recommend_papers.sh runs. Sorts by overlap count and citations.
No output schemas documented. LLMs cannot infer what fields to expect from tool responses, forcing unstructured parsing and breaking downstream tool chaining.
Destructive/write operations (download_paper, paper_text) lack confirmation patterns or dry-run support. Agents cannot safely preview side effects before committing.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). LLMs cannot distinguish safe reads from destructive writes, risking accidental data loss.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 50 | 2026-07-28+ | v2 |
Fetch a paper and write its full text to disk as clean, section-marked plaintext. Tries arXiv HTML, ar5iv HTML, then PDF fallback. Preserves headings, tables, and math.
Find papers similar to a given paper via Semantic Scholar recommendations. Useful when citation graph is sparse.
Produce a safe filename slug from a research topic. Lowercase, keeps only [a-z0-9-], collapses runs of '-', truncates to 40 chars.
Full-text passage search across 12M+ papers via Semantic Scholar. Returns exact paper passages with section information.
Check that every quotation in a report is real text from a paper that was actually fetched. Mechanical verification against local corpus.
Parameter descriptions are minimal (avg 40-60 chars). Many lack format constraints, ranges, or validation rules. E.g., 'limit' in crawl_arxiv has no min/max; 'date_from' has no format hint.
No error handling guidance. Tools fail silently or return raw shell/Python errors. LLMs receive no recovery hints (e.g., 'Try search_users() first' pattern).