Panda Research Tools
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TDQS
Scored across 6 tools
Each tool occupies a distinct slot in the research workflow: discover papers (literature_search), screen biomedical literature (pubmed_evidence_table), format citations (citation_formatter), audit references (reference_checker), find people (researcher_expert_finder), and pull tables from PDFs (pdf_table_extractor). Descriptions explicitly name the nearest neighbour and give a 'Do not use X, use Y' rule for the only genuinely close pairs (check vs. format, OpenAlex vs. PubMed). The residual overlap of literature_search/pubmed returning ready-made citations is a side output, not a competing use case.
All six names follow the same snake_case <subject>_<function> compound pattern (reference_checker, citation_formatter, literature_search, researcher_expert_finder, pdf_table_extractor, pubmed_evidence_table). No mixing of camelCase, verb-first, or vague one-word names; each name states the resource and the operation.
Six tools is well within the sweet spot and each maps to a clearly separate stage of a literature/research pipeline, so nothing feels redundant or padded. No obvious missing capability is being papered over by splitting or merging tools.
The surface covers the discovery → screening → verification → formatting loop plus two adjacent needs (expert finding, PDF table extraction), which is strong lifecycle coverage for a research toolkit. Minor gaps remain: no full-text/abstract-level reading or data-extraction tool for a specific paper, and no library/deduplication management across result sets, but agents can work around these via literature_search output and pdf_table_extractor.