PubMed Search

Analyze a search query without executing the search. Useful for understanding how unified_search will process your query before actually running it. Args: query: The search query to analyze Returns: Analysis including: - Complexity level (SIMPLE/MODERATE/COMPLEX/AMBIGUOUS) - Intent (LOOKUP/EXPLORATION/COMPARISON/SYSTEMATIC) - PICO elements (if detected) - Recommended sources - Recommended strategies

Build a citation tree (network) from a single article. 🌳 Creates a visual citation network showing research lineage: - Forward (citing): Who cites this paper? (newer research) - Backward (references): What does this paper cite? (foundational work) ⚠️ IMPORTANT: Only accepts ONE PMID at a time to control API load. For multiple papers, call this tool separately for each. 📊 Output Formats (output_format parameter): - "cytoscape": Cytoscape.js format (default, academic standard) - "g6": AntV G6 format (modern, high-performance) - "d3": D3.js force graph format (flexible, Observable) - "vis": vis-network format (simple, quick prototypes) - "graphml": GraphML XML (desktop tools: Gephi, yEd, VOSviewer) - "mermaid": Mermaid diagram (VS Code preview, Markdown) Args: pmid: Single PubMed ID (e.g., "12345678"). Only ONE PMID accepted - do NOT pass multiple. depth: How many levels to traverse (1-3, default 2). - depth=1: Direct citations/references only - depth=2: Also get citations of citations (recommended) - depth=3: Maximum depth (can be slow, ~100+ API calls) direction: Which direction to build the tree: - "forward": Only citing articles (who cites this) - "backward": Only references (what this cites) - "both": Both directions (default, recommended) limit_per_level: Max articles to fetch per node per level (default 5) output_format: Graph format for visualization (default "cytoscape") - "cytoscape": Cytoscape.js (academic standard, bioinformatics) - "g6": AntV G6 (modern, TypeScript, great for large graphs) - "d3": D3.js force layout (most flexible, Observable notebooks) - "vis": vis-network (simple and easy) - "graphml": GraphML XML (Gephi, VOSviewer, yEd, Pajek) - "mermaid": Mermaid diagram (preview in VS Code Markdown) Returns: Markdown summary followed by JSON with graph data in the requested forma…

Build a persisted, versioned, evidence-backed Research Chronicle. A chronicle is the durable record of how a research topic evolved, and the single entry point for research-evolution work (it replaces the older one-shot timeline tools). It is stored with a monotonic revision number, so re-running it later produces revision N+1 and you can diff revisions to see exactly what changed. The primary axis is chronological; research branches are a secondary organizing dimension. Both come from the same stored snapshot, and preserve shared provenance in `output="timeline"` and `output="tree"`. Agreement between projections is not independent evidence verification. Every entry carries: - a one-sentence claim with inline citations - supporting / contradicting / updating evidence articles - a research branch (lineage) assignment - provenance and a confidence score The typed provenance graph links Topic → Branch → Entry → EvidenceArticle and is validated against edge invariants. The audit reports evidence coverage, identifier coverage, branch coverage, graph integrity, and chronology gaps, so you always know how complete the picture is. Args: topic: Research topic (drug, gene, disease, intervention). Required unless `pmids` or a stored `chronicle_id` is supplied. pmids: Delimited PMIDs, a string array or JSON array string, or "last" to chronicle the previous search results instead of running a new search. max_events: Maximum timeline events to consider (topic mode). Omit to inherit the continued revision's value, else 30. min_year: Earliest publication year to include (topic mode). max_year: Latest publication year to include (topic mode). chronicle_id: Continue an existing chronicle (creates revision N+1) instead of deriving the ID from the topic. Passing it alone re-runs the stored scope, so the resulting diff shows research movement rather than a changed…

Configure your institution's link resolver for full-text access. ═══════════════════════════════════════════════════════════════════════════════ 🏛️ INSTITUTIONAL ACCESS CONFIGURATION ═══════════════════════════════════════════════════════════════════════════════ This tool configures OpenURL link resolver integration, allowing you to access paywalled articles through your institution's library subscription. Remote service callers may call this tool with no configuration arguments to inspect the operator-installed configuration, but cannot mutate the server-owned, deployment-wide OpenURL settings. Configure those at deployment time or from a trusted local server instead. ═══════════════════════════════════════════════════════════════════════════════ HOW IT WORKS: ═══════════════════════════════════════════════════════════════════════════════ 1. Your library subscribes to journals through publishers 2. Library provides a "Link Resolver" service (SFX, 360 Link, Primo, etc.) 3. OpenURL passes article metadata to the resolver 4. Resolver checks your subscriptions and provides full-text access ═══════════════════════════════════════════════════════════════════════════════ USAGE: ═══════════════════════════════════════════════════════════════════════════════ Option 1: Use a preset (easiest) ───────────────────────────────── configure_institutional_access(preset="ntu") Available presets: - 台灣: "ntu" (台大), "ncku" (成大), "nthu" (清大), "nycu" (陽明交大) - 美國: "harvard", "stanford", "mit", "yale" - 英國: "oxford", "cambridge" - 通用: "sfx", "360link", "primo" (需要 resolver_url) Option 2: Custom URL ───────────────────── configure_institutional_access( resolver_url="https://your.library.edu/openurl" ) Option 3: Disable ───────────────────── configure_institutional_access(enable=False) ═══════════════════════════════════════════════════════════════════════════════ FINDING YOUR RESOLVER URL: ═══════════════════════════════════════════════════════════════════════════════ 1.…

Query the curated ICD/MeSH crosswalk in one explicit direction. Use ``icd_to_mesh`` with one complete ICD-9-CM or ICD-10-CM code, or ``mesh_to_icd`` with a MeSH term. The returned mapping is a limited convenience crosswalk, not a substitute for a current licensed UMLS terminology service.

Permanently delete a saved pipeline configuration and execution history. For the default tenant, any process-level schedule is removed only after the stored pipeline deletion succeeds. Isolated tenants never mutate the shared process scheduler. Args: name: Name of the saved pipeline to delete. Returns: Confirmation of deletion.

Diagnose why institutional fulltext access succeeds or fails for an article. Runs up to three probes and reports each path's outcome: 1. **Direct fetch** (Phase 1, IP-aware) — follows ``https://doi.org/<doi>`` and classifies whether the publisher served fulltext, a paywall, or a login page. Works automatically when your network IP is on the publisher's institutional allow-list (campus / VPN). 2. **EZproxy fetch** (Phase 2, BYO-cookie) — rewrites the publisher hostname to your library's EZproxy host and replays your exported browser session cookie. Configured via env vars: - ``EZPROXY_HOST`` (e.g. ``ezproxy.lib.ntu.edu.tw``) - ``EZPROXY_COOKIE_FILE`` (path to browser-exported cookies.json) - ``EZPROXY_ENABLED=1`` 3. **OpenURL handoff** — generated for you to open manually in a browser when the automated paths fail. Usage: diagnose_institutional_access( source={"kind": "doi", "value": "10.1097/ALN.0000000000003599"} ) diagnose_institutional_access( source={"kind": "pmid", "value": "38353755"}, try_ezproxy=False ) Args: source: Exactly one PMID or DOI. A PMID is resolved to a DOI when possible so direct and EZproxy probes can run. try_direct: Run the Phase 1 direct probe (default True). try_ezproxy: Run the Phase 2 EZproxy probe (default True). Returns: Markdown report listing every probe's status, classification, and advice on the next action to take.

Fetch detailed information for one or more PubMed articles. Args: pmids: PubMed IDs - accepts multiple formats: - "12345678" (single) - "12345678,87654321" (comma-separated) - "PMID:12345678" (with prefix) - ["12345678", "87654321"] (list) - '["12345678", "87654321"]' (JSON array string) - Newlines, semicolons, pipes, and Chinese separators are also accepted. Inputs are string-only and fail as a complete batch when any PMID is invalid. Returns: Detailed information for each article.

Find articles that cite a given PubMed article. Uses PubMed Central's citation data to find papers that reference this article. ═══════════════════════════════════════════════════════════════ 📈 FORWARD CITATION SEARCH (Impact Tracking) ═══════════════════════════════════════════════════════════════ Direction: Source Paper → Papers that cite it (FORWARD in time) USE CASES: ────────── - 🔬 Track research impact: Who built on this work? - 📊 Find follow-up studies: What happened after this discovery? - 🔄 Identify controversies: Papers that challenge or refute findings - 📚 Literature review: Ensure you have the latest developments COMPLEMENTARY TOOLS: ──────────────────── - get_article_references(): BACKWARD search (what this paper cited) - find_related_articles(): Similar papers (topic-based, not citation-based) ═══════════════════════════════════════════════════════════════ EXAMPLE: ═══════════════════════════════════════════════════════════════ # Find papers that cite a landmark CRISPR paper find_citing_articles(pmid="23287718", limit=20) → Returns papers published AFTER 2012 that reference this work # Then analyze citation metrics get_citation_metrics(pmids="last") → See which citing papers are most influential Args: pmid: PubMed ID of the source article ("12345678" or "PMID:12345678"). limit: Maximum number of citing articles to return (1-100, default: 10). Returns: List of citing articles with details.

Find articles related to a given PubMed article. Uses PubMed's "Related Articles" feature to find similar papers. ═══════════════════════════════════════════════════════════════ 🔗 CITATION NETWORK EXPLORATION WORKFLOW ═══════════════════════════════════════════════════════════════ This is ONE of THREE tools for exploring citation networks: 1️⃣ find_related_articles() ← YOU ARE HERE │ 📌 Algorithm-based similarity (like PubMed "Similar Articles") │ 📌 Finds papers with similar topics, MeSH terms, authors │ 📌 Good for: Discovering related research you might have missed └─► Returns: Similar papers (not based on citations) 2️⃣ find_citing_articles() │ 📌 Forward citation search (who cited THIS paper?) │ 📌 Finds papers published AFTER the source article │ 📌 Good for: Tracking impact, finding follow-up studies └─► Returns: Papers that cite this article 3️⃣ get_article_references() │ 📌 Backward citation search (what did THIS paper cite?) │ 📌 Finds papers published BEFORE the source article │ 📌 Good for: Finding foundational papers, methodology sources └─► Returns: This article's bibliography ═══════════════════════════════════════════════════════════════ EXAMPLE WORKFLOW: ═══════════════════════════════════════════════════════════════ Step 1: Start with a key paper find_related_articles(pmid="23132851") → Find similar research directions Step 2: Explore backward (foundations) get_article_references(pmid="23132851") → Find the foundational papers it builds on Step 3: Explore forward (impact) find_citing_articles(pmid="23132851") → Find how the field developed after this paper Args: pmid: PubMed ID of the source article ("12345678" or "PMID:12345678"). limit: Maximum number of related articles to return (1-50, default: 5). Returns: List of related articles with details.

Gather search intelligence for a topic - returns RAW MATERIALS for Agent to decide. This tool provides the BUILDING BLOCKS for search, not finished queries. The Agent decides how to use them. ══════════════════════════════════════════════════════════════════════ TWO USAGE MODES: ══════════════════════════════════════════════════════════════════════ MODE 1: KEYWORD SEARCH (single topic) ───────────────────────────────────── User: "搜尋 remimazolam 的文獻" Step 1: generate_search_queries("remimazolam") Step 2: Build a Boolean query from returned materials Step 3: analyze_search_query(query="<combined_boolean_query>") Step 4: unified_search(query="<combined_boolean_query>") ���═════════════════════════════════════════════════════════════════════ MODE 2: PICO SEARCH (clinical question) ─────────────────────────────────────── User: "remimazolam 在 ICU 鎮靜比 propofol 好嗎？會減少 delirium 嗎？" Step 1: Agent extracts P/I/C/O from the clinical question, then calls validate_pico_plan(description=..., p=..., i=..., c=..., o=...) to validate the structured handoff and get a runnable PICO pipeline. Step 2: For EACH PICO element, call generate_search_queries() IN PARALLEL: - generate_search_queries("ICU patients") → P materials - generate_search_queries("remimazolam") → I materials - generate_search_queries("propofol") → C materials - generate_search_queries("delirium") → O materials Step 3: Combine materials using Boolean logic: High precision: (P_terms) AND (I_terms) AND (C_terms) AND (O_terms) Recall-oriented: (P_terms) AND (I_terms OR C_terms); validate against eligible seed papers Step 4: Add Clinical Query filter if appropriate: - filters="clinical_query:therapy" → 治療效果比較 - filters="clinical_query:diagnosis" → 診斷相關 - filters="clinical_query:prognosis" → 預後相關 - filters="clinical_query:etiology" → 病因相關 Step 5: Validate the final query with analyze_sea…

Get structured figure metadata (label, caption, image URL) and PDF links from a PMC Open Access article. Returns all figures with their captions and direct image URLs, plus PDF download links for the complete article. ``source`` is a discriminated identifier object, so the schema itself requires exactly one explicit PMID or PMCID. Args: source: {"kind":"pmcid","value":"PMC12086443"} or {"kind":"pmid","value":"40384072"}. include_subfigures: Parse sub-figures (e.g., Figure 3A, 3B) as separate entries. include_tables: Also extract tables rendered as images. Returns: Structured figure data with image URLs, captions, and PDF links. Example: get_article_figures(source={"kind":"pmcid","value":"PMC12086443"})

Get the references (bibliography) of a PubMed article. Returns the list of articles that this paper cites in its bibliography. This is the OPPOSITE of find_citing_articles: - get_article_references: Papers THIS article cites (backward in time) - find_citing_articles: Papers that cite THIS article (forward in time) ═══════════════════════════════════════════════════════════════ 📚 BACKWARD CITATION SEARCH (Foundation Discovery) ═══════════════════════════════════════════════════════════════ Direction: Source Paper → Papers it cited (BACKWARD in time) USE CASES: ────────── - 🏛️ Find foundational papers: Core works the field builds on - ⚗️ Methodology sources: Papers describing techniques used - 📖 Background reading: Build understanding of a topic - 🔍 Verify claims: Check sources for specific assertions ═══════════════════════════════════════════════════════════════ EXAMPLE WORKFLOW: ═══════════════════════════════════════════════════════════════ # Start with a recent review article get_article_references(pmid="38123456", limit=50) → Get the bibliography of this review # Find most-cited foundational papers get_citation_metrics(pmids="last", sort_by="citation_count") → Identify which references are the most influential # Read a foundational paper fetch_article_details(pmids="12345678") → Get full details of an important reference Args: pmid: PubMed ID of the source article ("12345678" or "PMID:12345678"). limit: Maximum number of references to return (1-100, default: 20). Returns: List of referenced articles with details.

Get citation metrics from NIH iCite for articles. Returns field-normalized citation data including: - citation_count: Total number of citations - relative_citation_ratio (RCR): Field-normalized metric (1.0 = average) - nih_percentile: Percentile ranking (0-100) - citations_per_year: Citation velocity - apt: Approximate Potential to Translate (clinical relevance 0-1) Can sort and filter results by citation metrics. Args: pmids: PubMed IDs - accepts multiple formats: - "12345678,87654321" (comma-separated) - ["12345678", "87654321"] (list) - '["12345678", "87654321"]' (JSON array string) - "PMID:12345678" (with prefix) - "last" to use PMIDs from the last search Batches are fail-closed and limited to 1,000 items before deduplication. sort_by: Metric to sort by: - "citation_count": Raw citation count (default) - "relative_citation_ratio": Field-normalized (recommended) - "nih_percentile": Percentile ranking - "citations_per_year": Citation velocity min_citations: Filter out articles with fewer citations min_rcr: Filter out articles with RCR below threshold (e.g., 1.0 = average) min_percentile: Filter out articles below percentile (e.g., 50 = top half) Returns: Articles with citation metrics, sorted and filtered as requested. iCite transport or response failures return an explicit retryable error and are never rendered as an empty/unindexed result.

Get detailed information about a compound by PubChem CID. Args: cid: PubChem Compound ID Returns: JSON with compound details including formula, SMILES, properties

Get PubMed articles linked to a compound. Uses NCBI's curated compound-to-publication links. Args: cid: PubChem Compound ID limit: Maximum PubMed IDs to return (1-100) Returns: JSON with linked PubMed IDs

🔥 Enhanced multi-source fulltext retrieval. Automatically tries multiple sources to find the best fulltext: 1. Europe PMC (if PMC ID available) 2. Unpaywall (finds OA versions via DOI) 3. Institutional direct/EZproxy fetch (when DOI-backed and enabled) 4. CORE (open-access repository metadata and available text) With extended_sources=True, also searches: 5. CrossRef (publisher links) 6. DOAJ (Gold OA journals) 7. Zenodo (research repository) 8. PubMed LinkOut (external providers) 9. Semantic Scholar, OpenAlex, arXiv, bioRxiv, medRxiv ``source`` is a discriminated identifier object, so the schema itself requires exactly one explicit PMID, PMCID, or DOI kind. Args: source: One object such as {"kind":"pmid","value":"12345678"}, {"kind":"pmcid","value":"PMC7096777"}, or {"kind":"doi","value":"10.1001/jama.2024.1234"}. sections: Filter body sections (e.g., "introduction,methods,results"). Missing titles are reported with available sections; abstracts are never substituted for missing body evidence. include_pdf_links: Include PDF download links (default: True). False skips link enrichment when structured fulltext is already available. include_figures: Include figure metadata with image URLs (default: False) extended_sources: Search the extended downloader chain after the standard policy (default: False) output_format: Response format - "markdown" (default), "json", or "toon" allow_browser_session: Control browser-session fallback. - True: force broker fallback when configured - False: disable broker fallback - None: use auto mode from broker configuration Returns: Fulltext content with PDF links from all available sources. Example: get_fulltext(source={"kind":"pmcid","value":"PMC7096777"}) get_fulltext(source={"kind":"doi","value":"10.1038/s41586-021-03819-2"})

Get detailed information about a gene by NCBI Gene ID. Args: gene_id: NCBI Gene ID (from search results or known) Returns: JSON with gene details including symbol, name, summary, location

Get PubMed articles linked to a gene. This uses NCBI's curated gene-to-publication links, which are more precise than keyword searches. Args: gene_id: NCBI Gene ID limit: Maximum PubMed IDs to return (1-100) Returns: JSON with linked PubMed IDs

Generate institutional access link (OpenURL) for an article. ═══════════════════════════════════════════════════════════════════════════════ 🔗 GET LIBRARY ACCESS LINK ═══════════════════════════════════════════════════════════════════════════════ Generate an OpenURL that will take you through your library's link resolver to access the full text of an article. PREREQUISITES: ───────────────── Must first call configure_institutional_access() to set up your resolver. USAGE: ───────────────── With PMID (easiest): get_institutional_link( source={"kind": "pmid", "value": "38353755"} ) With DOI: get_institutional_link( source={"kind": "doi", "value": "10.1001/jama.2024.1234"} ) With full metadata (most reliable): get_institutional_link( source={ "kind": "metadata", "title": "Some Article Title", "journal": "JAMA", "year": 2024, "volume": "331", "issue": "1", "pages": "45-52" } ) Args: source: Exactly one explicit PMID, DOI, or bounded metadata object. Returns: OpenURL link or error message

Get execution history for a saved pipeline. Shows past execution results with diff analysis: which articles are new compared to the previous run. Args: name: Name of the saved pipeline. limit: Maximum number of history entries to return (default: 5). Returns: Execution history with date, article count, new/removed articles, status.

Get text-mined annotations from Europe PMC. Returns entities extracted from the article text including genes, diseases, chemicals, organisms, and more. ``source`` is exactly one PMID or PMCID. Args: source: {"kind":"pmid","value":"12345678"} or {"kind":"pmcid","value":"PMC7096777"}. semantic_type: Filter by entity type. Options: - "GENE_PROTEIN": Genes and proteins - "DISEASE": Diseases and conditions - "CHEMICAL": Drugs and chemicals - "ORGANISM": Species and organisms - "GO_TERM": Gene Ontology terms - None: Return all types (default) Returns: List of text-mined entities with counts and sections.

List all saved pipeline configurations. Args: tag: Filter by tag (e.g., "sedation"). Empty = show all. scope: Filter by scope: "workspace", "global", or "" (show all). Returns: Table of saved pipelines with name, scope, description, tags.

List available institutional link resolver presets. ═══════════════════════════════════════════════════════════════════════════════ 📚 AVAILABLE RESOLVER PRESETS ═══════════════════════════════════════════════════════════════════════════════ These presets contain pre-configured URLs for common institutions. Use them with configure_institutional_access(preset="name"). Returns: List of available presets with URLs

Load a pipeline configuration for review or editing. Loads from either source: - Saved name: "weekly_remimazolam" or "saved:weekly_remimazolam" - Local-only file: "file:path/to/pipeline.yaml" (disabled for authenticated service callers) The returned YAML can be reviewed, modified, and then: - Executed directly: unified_search(pipeline="<yaml>") - Saved with changes: save_pipeline(name="...", config="<yaml>") Args: source: Pipeline source identifier (see above). Returns: Full pipeline YAML content + metadata.

Export citations to reference manager formats. ╔═══════════════════════════════════════════════════════════════════╗ ║ RECOMMENDED: Use source="official" (default) for best quality ║ ╚═══════════════════════════════════════════════════════════════════╝ ## When to Use - Exporting references to EndNote, Zotero, Mendeley - Creating BibTeX for LaTeX documents - Generating citation lists for manuscripts ## Source Options | Source | Formats | Quality | Speed | |------------|--------------------|------------|--------| | official | ris, medline, csl | ★���★★★ | Fast | | local | ris, bibtex, csv, medline, json | ★★★★ | Fast | ## Format Selection Guide - ris: EndNote, Zotero, Mendeley (official recommended) - medline: NBIB format for PubMed tools - csl: JSON for programmatic citation styling - bibtex: LaTeX documents (local only) - csv: Data analysis, Excel (local only) Args: pmids: Articles to export. Accepts: - "last" → results from previous search - "12345678,87654321" → comma-separated PMIDs - ["12345678", "87654321"] → list of PMIDs - '["12345678", "87654321"]' → JSON array string - "PMID:12345678" → with prefix format: Export format (default: "ris") - official API: ris, medline, csl - local only: bibtex, csv, json include_abstract: Include abstracts in output (default: True). False requires source="local"; official payloads are returned unmodified. source: Citation source (default: "official") - "official": NCBI Citation API (recommended, best quality) - "local": Local formatting (more formats, offline capable) Returns: JSON with status and export_text containing formatted citations. Examples: # Export last search results (recommended) prepare_export(pmids="last", format="ris") # Export specific PMIDs to BibTeX prepare_export(pmids="12345678,87654321", format="bibtex", source="local")…

Analyze a scientific figure or image for literature search. ═══════════════════════════════════════════════════════════════════════ 🔬 VISION-TO-LITERATURE SEARCH (Experimental) ═══════════════════════════════════════════════════════════════════════ This tool enables searching for scientific literature based on images. WORKFLOW (the host agent performs the analysis and search): ───────────────────────────────────────────────────────── 1. Provide an image (URL or base64-encoded) 2. This tool returns the image using MCP ImageContent protocol 3. YOU (the Agent) analyze the image using your vision capabilities 4. Extract relevant ENGLISH search terms from the image 5. Call `search_biomedical_images()` or `unified_search()` with extracted terms when literature retrieval is within the user-requested scope 6. Return both the analysis and search results to the user ⚠️ IMPORTANT RULES: ──────────────── - ALL search queries must be in ENGLISH (Open-i requirement) - This tool returns an image and guidance; it does not invoke a vision model - The host agent controls any subsequent search within its permissions - If the image shows a medical condition, extract the medical term in English SEARCH TYPES: ───────────── - "comprehensive": General analysis, extract all relevant terms (default) - "methodology": Focus on methods, equipment, techniques shown - "results": Focus on data, graphs, statistical findings - "structure": Focus on molecular/chemical structures - "medical": Focus on clinical/medical imaging findings USE CASES: ────────── - 📊 Scientific figures → Find papers with similar data/charts - 🔬 Microscopy images → Find related research - 🧬 Molecular structures → Find papers about the compound - 📈 Graphs/plots → Find papers with similar analyses - 🏥 Medical images → Find case reports or clinical studies - ⚗️ Lab equipment → Find methodology papers IMPORTANT: ────────── Image observations are search hypotheses that require source verification. Follow the user…

Read stored Research Chronicles: load, list, diff, narrate, analyze, compare. This is the read facade over chronicles created by `build_research_chronicle`. Chronicles persist across sessions, so you can revisit a topic weeks later and see precisely what moved. Because the evidence is already stored, analysis and comparison are instant and do not re-run any search. Actions: - "load": read one revision (defaults to latest) in any output format - "list": list stored chronicles, most recently updated first - "diff": compare two revisions — added, not observed/removed from the later view, and updated entries, plus evidence churn, branch churn, and the audit status transition. Absence does not prove retirement. - "narrate": render evidence-backed Markdown where every claim carries its entry ID and article identifiers - "milestones": entry-type and status distribution, per-year activity, evidence quality, and landmark entries for one chronicle - "compare": compare 2-5 chronicles side by side, including the evidence articles they share The required ``request`` discriminator makes invalid field combinations unrepresentable. ``compare`` takes one typed ``selection`` containing either 2-5 topic strings or 2-5 Chronicle IDs. Returns: Markdown or JSON text depending on the action and output format. Examples: read_research_chronicle(request={"action":"list"}) read_research_chronicle(request={"action":"load","chronicle_id":"remimazolam-9f2b1c4d","output":"tree"}) read_research_chronicle(request={"action":"diff","chronicle_id":"remimazolam-9f2b1c4d","from_revision":1}) read_research_chronicle(request={"action":"narrate","chronicle_id":"remimazolam-9f2b1c4d","mode":"full"}) read_research_chronicle(request={"action":"milestones","chronicle_id":"remimazolam-9f2b1c4d"}) read_research_chronicle(request={"action":"compare","selection":{"kind":"topics","values":["remimazolam","propofol"]}})

Read session data through one schema-exact discriminated request. Actions: - pmids: return PMIDs for one recorded search - article: return one cached article payload - summary: return current session summary and optional history - list_artifacts: list persistent MCP output artifact manifests - artifact: read one persistent artifact by artifact_id or artifact_uri - search_runs: list durable unified_search run envelopes - search_run: read one run by stable run_id - replay_search: return credential-free unified_search replay arguments Each action accepts only its own fields. For remote artifact reads, select an artifact_id or artifact_uri locator and use artifact_file plus offset/max_chars to page through large files. Local paths remain redacted unless both include_local_paths and the server setting allow them.

Save searched articles as guided local wiki/Foam/Markdown notes. ## When to Use - After unified_search, persist the selected literature into a local note library. - Give agents a structured alternative to generic write_file calls. - Create wiki notes with Foam-compatible wikilinks, MedPaper-like reference notes, and frontmatter. - Use stable wiki/Foam link targets and return wiki_validation for unresolved-link checks. ## Local Directory Resolution 1. output_dir argument, if provided 2. PUBMED_NOTES_DIR environment variable 3. PUBMED_WORKSPACE_DIR/references 4. PUBMED_DATA_DIR/references ## Authenticated Service Boundary Remote authenticated callers cannot choose output_dir or template_file. Their notes always go to references/ under the current tenant's installed SessionManager data root; process-wide notes/workspace environment paths are intentionally ignored. Args: pmids: Articles to save. Accepts "last", delimited PMID text, a string array, or a JSON array string. output_dir: Optional target folder for notes. note_format: "wiki" (default, Foam-compatible), "foam", "markdown", or "medpaper". include_abstract: Include abstracts in article notes. overwrite: Overwrite existing per-article notes when filenames collide. create_index: Create a collection index note linking saved articles. collection_name: Optional title/file stem for the index note. template_file: Optional Markdown template with placeholders like {title}, {pmid}, {citation_key}. include_csl_json: Write references.csl.json beside notes for citation-manager handoff. Returns: JSON with written/skipped files, index information, and wiki_validation. Local callers receive filesystem paths. Authenticated callers receive tenant-relative logical locators and never receive server host paths. Examples: save_literature_notes(pmids="last") save_literature_notes(pmids="last", note_format="medpaper", output_dir="./references") save_literature_notes(pm…

Save a pipeline configuration for later reuse. The config format is identical to unified_search's pipeline parameter (YAML or JSON). Saved pipelines can be loaded later by name: unified_search(pipeline="saved:weekly_remimazolam") Args: name: Unique identifier (alphanumeric + hyphens/underscores, max 64 chars). Overwrites if name already exists (upsert semantics). config: Pipeline YAML/JSON string. Same format as unified_search pipeline param. tags: Bounded array of canonical tags (e.g., ["anesthesia", "sedation"]). description: Human-readable description of the pipeline's purpose. scope: Storage scope - "workspace" (project-level, git-trackable), "global" (user-level, cross-project), or "auto" (workspace if available, otherwise global). Default: "auto". Returns: Confirmation with pipeline metadata.

Schedule a saved pipeline for periodic execution. Args: name: Saved pipeline name. cron: Required 5-field cron expression. Example: "0 9 * * 1" (Mon 9am). diff_mode: When True, store diff-mode preference with the schedule. notify: When True, store notify preference with the schedule. Returns: Schedule confirmation or removal result.

🖼️ Search biomedical images from NLM Open-i. Searches medical/scientific images from Open-i and returns image URLs with metadata (caption, article info, MeSH terms). ═══════════════════════════════════════════════════════════════ ⚠️ CRITICAL - LANGUAGE REQUIREMENT: ═══════════════════════════════════════════════════════════ Open-i ONLY supports English queries. If the user queries in non-English (Chinese, Japanese, Korean, etc.), you MUST: 1. Translate the query to English medical terminology first 2. Then call this tool with the English query Example: "喉頭水腫" → "laryngeal edema" "胸部X光肺炎" → "chest X-ray pneumonia" The tool has built-in translation hints for common CJK medical terms, but YOU should always verify the translation is correct. ═══════════════════════════════════════════════════════════ SOURCES: ═══════════════════════════════════════════════════════════════ - Open-i (NLM): X-ray, microscopy, clinical images ═══════════════════════════════════════════════════════════════ EXAMPLES: ═══════════════════════════════════════════════════════════════ General image search: search_biomedical_images("chest pneumonia CT scan") X-ray only: search_biomedical_images("fracture", image_type="x") Microscopy images: search_biomedical_images("histology liver", image_type="mc") Clinical teaching images (MedPix): search_biomedical_images("pneumothorax", collection="mpx") Case reports with CC-BY license, sorted by date: search_biomedical_images( "lung cancer", article_type="cr", license_type="by", sort_by="d" ) Cardiology specialty images: search_biomedical_images("echocardiogram", specialty="c") Video content only: search_biomedical_images("surgery technique", video_only=True) ═══════════════════════════════════════════════════════════════ Args: query: Search query (e.g., "chest X-ray pneumonia") image_type: Filter by image type (Open-i only): Positive filters…

Search ClinVar for clinical variants. ═══════════════════════════════════════════════════════════════ USE CASES: ═══════════════════════════════════════════════════════════════ - Look up clinical significance of genetic variants - Find variants associated with diseases - Research gene-disease associations - Get variant pathogenicity classifications Args: query: Gene name, variant, or disease condition limit: Maximum results (1-50) Returns: JSON with variant records including significance and conditions

Search PubChem for chemical compounds. ═══════════════════════════════════════════════════════════════ USE CASES: ═══════════════════════════════════════════════════════════════ - Look up drug/compound information - Find molecular formula and structure - Get compound synonyms and identifiers - Research chemical properties Args: query: Compound name or description limit: Maximum results (1-50) Returns: JSON with compound records including names, formulas, properties

Search NCBI Gene database for gene information. ═══════════════════════════════════════════════════════════════ USE CASES: ═══════════════════════════════════════════════════════════════ - Look up gene function and description - Find gene aliases and official symbols - Get chromosome location - Find genes by name or function Args: query: Gene name, symbol, or function keyword organism: Filter by organism (e.g., "human", "Homo sapiens", "mouse") limit: Maximum results (1-50) Returns: JSON with gene records including symbols, names, locations

Test your institutional link resolver configuration. ═══════════════════════════════════════════════════════════════════════════════ 🧪 TEST INSTITUTIONAL ACCESS ═══════════════════════════════════════════════════════════════════════════════ Tests if your configured link resolver is: 1. Properly configured 2. Reachable (network connection) 3. Returns a valid response NOTE: This only tests if the resolver endpoint is reachable. Actual full-text access depends on your institution's subscriptions. ═══════════════════════════════════════════════════════════════════════════════ FREE TEST OPTIONS: ═══════════════════════════════════════════════════════════════════════════════ If you don't have institutional access, you can test with: 1. Use "test_free" preset (EBSCO public resolver): configure_institutional_access(preset="test_free") test_institutional_access() 2. Most university resolvers will respond even without VPN, they just won't provide full-text (shows "Access options" page) Args: pmid: PMID to use for testing (default: 38353755) Returns: Test results including: - Configuration status - Network reachability - Generated OpenURL - Link to test manually

🔍 Unified Search - Single entry point for multi-source academic search. Automatically analyzes your query and searches the best sources. No need to choose between PubMed, OpenAlex, CrossRef, etc. ═══════════════════════════════════════════════════════════════════ WHAT IT DOES: ═══════════════════════════════════════════════════════════════════ 1. Analyzes your query (complexity, intent, PICO elements) 2. Automatically selects best sources based on query type 3. Searches multiple sources in parallel 4. Deduplicates and merges results 5. Ranks by configurable criteria 6. Enriches with OA links (Unpaywall) 7. Auto-detects ICD-9/10 codes and expands to MeSH terms 8. Optionally searches preprints (arXiv, medRxiv, bioRxiv) ═══════════════════════════════════════════════════════════════════ EXAMPLES (most calls only need 1-2 params): ═══════════════════════════════════════════════════════════════════ Simple (1 param): unified_search("remimazolam ICU sedation") With limit (2 params): unified_search("machine learning in anesthesia", limit=20) Specify sources: unified_search("CRISPR gene therapy", sources="pubmed,openalex") Auto minus one source: unified_search("sepsis biomarkers", sources="auto,-semantic_scholar") Search all enabled sources except enrichment-only CrossRef: unified_search("icu sedation", sources="all,-crossref") Clinical filters: unified_search("diabetes treatment", filters="year:2020-2025,age_group:aged,clinical_query:therapy") Include preprints + shallow search: unified_search("COVID-19 vaccine", options="preprints,shallow") Provider-native semantic retrieval (OpenAlex capability): unified_search("mechanisms of treatment resistance", sources="openalex", options="native_semantic") Reproducible systematic retrieval (bulk/cursor where supported): unified_search("melanoma AND immunotherapy", sources="openalex,semantic_scholar", options="sys…

Remove the active schedule for a saved pipeline. Args: name: Saved pipeline name whose schedule will be removed. Returns: Removed schedule metadata, or a native MCP error when none exists.

Validate agent-provided P/I/C/O and return a runnable PICO pipeline. ``question_type`` and ``profile`` are closed enums. ``sources`` is an explicit array of supported unified-search providers; malformed values fail instead of being silently replaced. When ``question_type`` is omitted, the application service infers it from the clinical question.

Verify a plain-text reference list against PubMed evidence. First version scope: - Reference-list verification only - Client supplies the extracted reference list text - Backend parses entries and resolves them via PMID / DOI / ECitMatch Second version scope: - Adds unresolved review workflow for ``partial_match`` and ``unresolved`` rows - Returns a manual-review queue with retry queries and review checklist - Supports human-in-the-loop acceptance/rejection in client-side workflows Args: reference_text: Plain-text references, ideally one per line or a numbered reference list extracted from a file. Limited to 200,000 characters / 400,000 UTF-8 bytes; each entry is limited to 4,000 characters / 8,000 UTF-8 bytes. source_name: Optional single-line file label for reporting (up to 255 characters / 512 UTF-8 bytes). max_references: Hard input-entry limit from 1 through 200. Inputs above the selected limit are rejected instead of truncated. Returns: JSON verification report with parsed fields, matched PubMed evidence, per-reference verification status, and explicit ``source_unavailable`` / ``not_checked`` rows when evidence could not be assessed.