Sider Scholar

When a user wants to create a document that they intend to save for future use, this tool should be invoked to analyze and generate the document according to the user's instructions; after the user confirms, the save function will then be called. Otherwise, the save function should not be invoked without the user's permission.

prepare_chat_for_saving

The model **must not actively invoke this tool** during the conversation. This tool is only intended to be triggered by the frontend via `window.openai.calltool` **after** `chat-save-preparation` has been called, and it is used **solely for saving files that have already been edited**. Therefore: - The model **must not call this tool directly** in its reasoning or responses. - The timing and logic for invoking this tool are **entirely controlled by the frontend**, and are independent of any tool calls produced by the model.

save_edited_document

🎯 **SCHOLAR'S PRIMARY SEARCH TOOL** - Use this FIRST for all academic searches. Cheapest option, fast, and covers 250M+ papers from Scholar's OpenAlex integration. **What it does:** - Direct search of OpenAlex database (world's largest open catalog of scholarly works) - Works best with simple keyword searches (2-5 keywords) - Supports optional simple filters (mostly used WITHOUT filters) - Allows sorting by date, citations, or relevance - Returns comprehensive paper metadata with DOIs, PDFs, citations, authors, institutions **⭐ DEFAULT CHOICE:** Use this for 90% of academic searches! **When to use (ALWAYS try this first):** - User wants papers on any topic: "find papers about brain-computer interfaces" - Simple keyword searches (you extract keywords from user query) - Basic year filtering if needed (one filter dimension) - Sorting by citations or date - ANY academic search unless it requires complex multi-dimensional filtering **How to use:** - query: Simple keywords (2-5 words) - YOU extract these from user's natural language Examples: "brain computer interface", "climate change mitigation", "CRISPR gene editing" - filters: OPTIONAL - Use sparingly, usually OMIT this Simple examples: {"from_publication_date": "2024-01-01"} or {"institutions.display_name": "MIT"} Database filter: {"locations.source.display_name": "PubMed"} or "arXiv" or "bioRxiv" - sort: "publication_date:desc" (newest first), "cited_by_count:desc" (most cited), or null for relevance - per_page: 25-200 (default 25) **Returns:** {meta: {count, per_page, page}, results: [{id, doi, title, authors, abstract, citations, pdf_url, institutions, credibility}]} **🎯 CRITICAL WORKFLOW:** 1. User asks for papers → extract keywords → search_open_access_works (CHEAPEST) 2. If needs complex multi-filter → smart_open_access_search (EXPENSIVE) 3. Never use smart_open_access_search for simple queries **Database-specific searches:** Use filters to target specific databases: filters={"locations.source.display_name": "PubMed"/"arXiv"/"bioRxiv"} This replaces pubmed_search, arxiv_search, biorxiv_search for most cases! **💰 COST:** Cheapest option - No extra API costs, no AI parsing needed.

search_open_access_works

⚠️ RARELY NEEDED - Use search_open_access_works with PubMed filter instead! Only use this for NCBI-specific databases beyond PubMed. **PREFERRED ALTERNATIVE:** search_open_access_works(query="your keywords", filters={"locations.source.display_name": "PubMed"}) This gives better results and includes OpenAlex metadata! **What it does:** - Direct search of NCBI databases (PubMed, PMC, Gene, Protein, etc.) - Returns PubMed-specific summaries with PMIDs - Provides MeSH terms and NCBI-specific metadata **When to use (RARE):** - User explicitly asks for "NCBI Gene database" or "NCBI Protein database" (non-PubMed NCBI) - Need MeSH terms specifically - User wants to search PMC, Gene, or Protein databases - search_open_access_works fails after trying with PubMed filter **When NOT to use:** - General medical/biological paper searches → use search_open_access_works - User says "find PubMed papers" → use search_open_access_works with PubMed filter (better!) **Parameters:** - query: Search query - database: "pubmed" (default), "pmc", "gene", "protein" - max_results: Default 10 **Returns:** {search_results: {count, query_key, webenv}, summaries: {pmid: {metadata}}} **🎯 WORKFLOW:** 1. User wants PubMed → search_open_access_works with PubMed filter 2. Only use pubmed_search if they need Gene/Protein databases

pubmed_search

⚠️ RARELY NEEDED - Use search_open_access_works with arXiv filter instead! **PREFERRED ALTERNATIVE:** search_open_access_works(query="your keywords", filters={"locations.source.display_name": "arXiv"}) This gives better results with OpenAlex metadata! **What it does:** - Direct search of arXiv preprint repository - Covers physics, math, CS, and related fields - Returns arXiv-specific IDs and metadata **When to use (RARE):** - User explicitly says "search arXiv directly" or "use arXiv API" - search_open_access_works with arXiv filter fails - Need arXiv-specific XML metadata **When NOT to use:** - User wants arXiv papers → use search_open_access_works with arXiv filter (better!) - General physics/CS/math search → use search_open_access_works **Parameters:** - query: Search terms - start: Pagination index (default 0) - max_results: Number of results (default 10) **Returns:** {status, data: [{id, title, summary, authors, published, pdf_url}]} **🎯 WORKFLOW:** User wants arXiv → search_open_access_works + arXiv filter (preferred)

arxiv_search

⚠️ RARELY NEEDED - Use search_open_access_works with bioRxiv filter instead! **PREFERRED ALTERNATIVE:** search_open_access_works(query="your keywords", filters={"locations.source.display_name": "bioRxiv"}) Better results with OpenAlex metadata! **What it does:** - Direct search of bioRxiv preprint server - Title-only matching (limited) - Has rate limits **When to use (RARE):** - User explicitly says "search bioRxiv directly" - search_open_access_works with bioRxiv filter fails **When NOT to use:** - User wants bioRxiv papers → use search_open_access_works with bioRxiv filter (better!) **Parameters:** - query: Search terms (title matching) - year_start/year_end: Year range - sorted: true for date sort - count: Results (default 20) **Returns:** {query, results: [{paper metadata}]} **🎯 WORKFLOW:** User wants bioRxiv → search_open_access_works + bioRxiv filter (preferred)

biorxiv_search

⚠️ EXPENSIVE TOOL - Use ONLY when complex multi-dimensional filtering is required. Prefer search_open_access_works for simple searches. **What it does:** - Uses AI (OpenAI API call - COSTS MONEY) to parse natural language into structured OpenAlex filters - Automatically determines complex filter combinations across multiple dimensions - Handles queries requiring simultaneous filtering by year + citations + institution + author + journal - Searches OpenAlex with AI-optimized parameters **⚠️ COST WARNING:** Each call makes an OpenAI API request. Use sparingly! **When to use (RARE cases only):** - User needs COMPLEX filtering with 3+ dimensions that would be hard to manually specify - Query requires intelligent interpretation of ambiguous filter requirements - Example: "highly cited papers on AI from top universities published after 2020" (needs citation filter + institution filter + date filter) - User query has multiple contradictory or nuanced filtering requirements **When NOT to use (MOST cases):** - Simple keyword searches → use search_open_access_works (FREE, FAST) - Basic year filtering → use search_open_access_works with filters param - Single filter dimension → use search_open_access_works - User just wants "papers about X" → use search_open_access_works **How to use:** - query: Natural language research question (AI will parse it) - per_page/page: Pagination (defaults fine) **Returns:** Same as search_open_access_works - list of papers with metadata. **🎯 PRIORITY:** ALWAYS try search_open_access_works FIRST. Only use this if the query genuinely requires complex multi-dimensional filtering that you cannot construct manually.

smart_open_access_search

An agentic RAG (Retrieval-Augmented Generation) system for iterative, multi-round knowledge retrieval. ⚠️ CRITICAL: This is NOT a single-call tool. You MUST call it multiple times (up to 20 rounds allowed). **MANDATORY ITERATION PROTOCOL:** 1. Call the tool with your initial query 2. Receive 'passages' and 'should_continue' flag 3. IF 'should_continue' is TRUE → YOU MUST CALL AGAIN WITH A DIFFERENT QUERY - Read and analyze ALL passages to understand what information you got - Identify what's MISSING or unclear from current passages - Generate a COMPLETELY DIFFERENT query from a NEW ANGLE - Call the tool again immediately - Do NOT answer the user's question yet 4. IF 'should_continue' is FALSE → You have enough information to answer **🔥 CRITICAL: Query Diversity Requirements** Each new query MUST be SUBSTANTIALLY DIFFERENT from previous ones to avoid duplicate results: ❌ BAD (too similar): - Round 1: "user book acquisition" - Round 2: "user acquiring books" ← Same concept, will get duplicate results! ✅ GOOD (different angles): - Round 1: "user book acquisition" → general discussion - Round 2: "PDF reader features personalization" → technical features angle - Round 3: "reading behavior patterns analysis" → behavioral angle - Round 4: "book recommendation algorithms" → algorithmic angle **How to generate DIVERSE queries:** 1. **Different aspects**: If Round 1 was about "what", try "how", "why", "when", "who" 2. **Different granularity**: Broad overview → specific details → edge cases 3. **Different perspectives**: User view → system view → business view 4. **Different domains**: Features → behavior → architecture → performance 5. **Complementary info**: If got "benefits", ask about "challenges" or "implementation" **Required analysis before each new query:** Before calling again, you MUST: 1. Summarize key themes from current passages 2. List what topics/angles are MISSING 3. Choose ONE missing angle to explore 4. Formulate query targeting ONLY that new angle 5. Ensure new query uses DIFFERENT keywords than before **Example multi-round with diverse queries:** Round 1: "user book acquisition methods" → Got: General discussion about users getting books ✓ Covered: Basic concepts, user workflows ✗ Missing: Specific features, technical implementation Round 2: "PDF reading features personalization customization" → Got: Feature descriptions, customization options ✓ Covered: Features, UI/UX aspects ✗ Missing: User behavior data, usage patterns Round 3: "reading behavior PDF vs web statistics" → Got: Behavioral comparisons, usage statistics ✓ Covered: User behavior, comparison data ✗ Missing: Technical architecture, system design Round 4: "knowledge base architecture search retrieval" → Got: System design, technical implementation ✓ should_continue = false → NOW comprehensive coverage **REMEMBER:** - Same query = same results = wasted call! - Each query should explore a NEW dimension/angle - Analyze passages content, not just count - should_continue: true = MANDATORY different query - should_continue: false = Stop and answer - more then 5 rounds allowed

wisebase_rag_answer

get file list from Wisebase

wisebase_file_list

upload file to Wisebase

wisebase_upload