An open-source, model-agnostic AI workbench for scientific discovery with MCP server integration for notebooks, literature, artifacts, and skills
Open-Science exposes 17 tools across review, literature, and artifact domains. Most tools have descriptions (15/17), but quality is highly variable. Many descriptions are verbose and lack actionable structure for LLM selection. Input schemas are present but sparse: 8 tools have no visible input parameters, 9 have partial schemas. Parameter descriptions are inconsistent, some detailed, others missing entirely. No tool annotations (readOnlyHint/destructiveHint) despite clear risk classifications. Error handling and recovery guidance are absent. Output schemas are undocumented. This is a domain-specific tool suite with reasonable coverage but significant gaps in LLM-optimization and schema completeness.
Find and download one public full-text PDF into Literature Inbox for user review. Supply exactly one ref or candidate.
Format and attach an existing DOCX containing semantic {{cite:itemId}} and optional {{bibliography}} markers.
Format trusted Library records for the requested citation style and locale. Use returned citations verbatim.
Discover figures, tables and algorithms in structure caches for PDFs linked to this message.
Prepare and attach an existing UTF-8 LaTeX file containing semantic citation markers.
Return the execution log for the audited turn or one in-scope activity.
Read one complete Library abstract or a bounded batch of up to five abstracts. Pass exactly one of itemId or itemIds.
8 tools have no visible input schema (read_turn, submit_findings, list_pdf_elements, read_pdf_element, request_skill_import, and 3 others).
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite explicit risk classifications (READ_ONLY, WRITE). LLMs cannot infer safety properties without annotations. Missing from all 17 tools.
read_artifact description is 280+ characters and verbose, mixing multiple concerns (trace, content, format options, image handling). Exceeds LLM-optimized range (50-200 chars). Lacks clear WHEN-to-use guidance.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 46 | 2026-07-28+ | v2 |
Read one artifact attached to the audited turn. Use trace for execution, generation, producer, method, inputs, and existence without file bytes. Use targeted content for final visual, presentation, text, or value claims; omitted view remains content. XLSX targets use sheet/rowStart/rowEnd/columns; PDF/DOCX pages and PPTX slides use pages. A partial response is sufficient when the returned targets fully cover the claim. Set includePreview for a bounded rendered page/slide image when text alone cannot verify a visual claim. Supported raster images are MCP image blocks, never base64 JSON; unsupported format or model capability only limits Coverage and is not itself a finding.
Read bounded prose passages from PDFs explicitly linked to the current message. Use list_pdf_elements for figures and tables.
Retrieve bounded prose passages with page numbers from one Library PDF after finding the record with search_library.
Read exact cached evidence selected by list_pdf_elements, including figure or table evidence.
Return the ordered message and tool-activity blocks in the audited turn.
Request to import a skill into the Open-Science environment.
Save one to ten literature discoveries to the user Literature Inbox for review. Do not include origin fields.
Browse or search the user's Open-Science Literature Library. Scope defaults to the trusted current Project.
Complete the Review with one accepted structured submission, then stop. Correct and retry validation errors within the same Review Turn; a second accepted submission is prohibited. In an initial review with no checkable claims, submit empty checks; tracked re-reviews follow their strict run prompt.
Attach a generated image, chart, report, data export, or archive to the current Open-Science response. The file must already exist before using a localPath source.
Output schemas are undocumented for all tools. LLMs cannot plan downstream calls or extract fields without knowing return structure. No documented pagination, field names, or result limits.
No error handling or recovery guidance. Tools like submit_findings (WRITE) and acquire_pdf (WRITE) lack confirmation steps, dry-run modes, or actionable error messages. Agents cannot self-correct on failure.