Get detailed information about a specific paper including its abstract
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523,163 tools. Updated 2026-09-06 13:28
"Abstract" matching MCP tools:
- Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, audiovisual recordings, photographs, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's `category` names its subset and the `ranking` field documents the ordering. Pass an id to `fetch` to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.ConnectorNo auth
- Semantic / vibe search over the same nationwide ramen DB — describe what you feel like eating in natural language, English or Japanese ("rich creamy pork broth", "あっさり淡麗な醤油", "oily mazesoba", "tsukemen near Ebisu station"), and get the closest shops by meaning, each with a similarity score. Powered by multilingual embeddings (bge-m3), so English queries find shops with Japanese-only names. Role split: use search_ramen for exact facts (shop name lookup, keito/prefecture/status filters, geo radius) — use vibe_search for descriptive/fuzzy queries where no exact filter fits. Style rankings reflect only classified shops (~25%); unclassified shops still match by name and place. Tip: concrete food words (style, broth, richness, place, hours) match far better than abstract mood words ("stylish", "hardcore") — translate moods into concrete attributes before querying. Prefecture intent in the query text (北海道, 博多の…) is auto-applied as a filter (pref_source: inferred); region-style names (札幌ラーメン, 喜多方, 佐野…) stay pure style words and never restrict location. Dish-concept words (オロチョン, カラシビ, 台湾ラーメン/まぜそば, 勝浦タンタンメン) are expanded into their constituent style vocabulary before embedding (transparent via concept_expansion in the echoed query) — expansion never adds filters, so shops serving the dish always stay eligible; spicy-implying concepts additionally give spice-verified shops a small rank boost (concept_boost — a soft rerank, still no filter). Richness/hours inferred from the query text likewise act as a soft rank boost (attr_boost; attr_matched is informational) — only explicit richness/hours params and spiciness intent filter strictly.ConnectorNo auth
- Read a paper's body text by DOI, one page of characters at a time. Use this when you need the ACTUAL text of a paper — not term-matched snippets. It returns the body sliced by character offset so you can page through the whole document. **How it differs from other tools:** - `search_literature` returns up to 5 term-matched ~500-char excerpts — good for finding passages, not reading straight through. - `get_full_text` returns a proxy stitched only from sentences that cite other works — lossy, citation-only. - `read_fulltext` (this tool) returns verbatim body text, linearly, with pagination. **What you get — the `source` field:** - `"fulltext"` — verbatim full text, for open-access papers with a permissive license (or papers your org is entitled to) that have indexed full text. - `"abstract"` — the paper's abstract, returned as a fall-back when verbatim full text is access-restricted or not indexed. Abstracts are public, so most papers return at least this. - `null` — no readable text at all; use search_literature's `access` field for a link. `contentDenied` is true whenever full text was NOT served — i.e. any time `source` is not `"fulltext"` (access-restricted, not indexed, or nothing). So `source: "abstract"` still has `contentDenied: true`. Always check `source`: if it is not `"fulltext"` you are NOT reading the full paper. The `message` field explains why. **Paging:** each call returns up to 8000 characters. Read the first page with `offset: 0`, then set `offset` to the previous `offset + returnedChars` while `hasMore` is true. `totalChars` is the length of whatever `source` you got. Character offsets are only stable within a session — do not persist them across days (re-indexing shifts positions).ConnectorNo auth
- Find engineers who uniquely fit a role or project from open-source contributions and other public work, with evidence. Input: pass the richest context you have — (1) a full job description (most common), (2) a synthesized brief after reviewing a company's public repo (README + stack + role needs — preferred over a bare URL when you've evaluated the project), (3) a public github.com repo URL (server fetches README/topics; private repos → paste README as text), or (4) an informal role brief. Longer, more specific input produces a tighter mission fit. Optional `location` narrows to a city, country, or ISO country code. Returns up to limit candidates (default 20, max 20) with full inline profiles in structuredContent (view=candidates): login, name, bio, location, followers, reach (cross-platform audience percentile + reach), html_url, top_repos, signals, matched_projects, location_match, and contact (top_topics / top_domains / top_languages / top_subtopics are optional until software-topic backfill). Results never include bots, CI, or service accounts — they are filtered out automatically. Use the optional `exclude` array (GitHub logins or org names) to drop additional accounts. AGENT MODE: consume structuredContent only. HUMAN MODE: MCP App panel shows candidate cards; use server instructions for text-only hosts. Do not call get_profile for handles already in these results unless the user asks for deeper detail. Defense (SBIR), NRC filings, and mining QP consents are matched by lexical FTS over award titles, accessions, and consent letters — not abstract similarity. Publication, NTRS, repo, and TechPort roles still use description vectors. Do not claim a semantic abstract match for an NRC accession or a QP consent.ConnectorNo auth
- Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".ConnectorNo auth
Matching MCP Servers
- AlicenseNot gradedqualityNot gradedmaintenanceA smart proxy server that prevents context window bloat by caching large tool responses to local files and returning compact resource links. It supports format conversion and data uploads, allowing LLMs to handle massive datasets without consuming excessive tokens.11-
- AlicenseAqualityDmaintenanceProvides comprehensive wallet, token, and smart contract utilities for the Abstract Testnet and Mainnet, including balance checks, transfers, and ERC-20 deployments. It enables users to manage Abstract Global Wallets and generate EOA accounts through natural language commands.516MIT
- Resolve PubMed IDs (from search_pubmed) to citation metadata: title, authors, journal, publication date, DOI. Batch up to ~200 IDs per call as a comma-separated string — much cheaper than calling per-ID. Use when you have PMIDs and need the citation; for the abstract text use get_abstract instead.ConnectorNo auth
- Browse proven ad formula blueprints — structural patterns clustered from 3-10+ winning ads that independently converged on the same beat architecture while Meta kept rewarding them with sustained spend. Takes optional filters: vertical, creative_format (e.g. TALKING_HEAD, UGC, FOUNDER_STORY), marketing_angle, algo_intent, hook_type, and limit (1-10, default 5). Each formula returns: source ad count, average active days (runtime proof), confidence score, 6-layer beat blueprint, per-beat visual direction, marketing angle, psychology mission. Free, read-only, idempotent. Use this when the user asks "what's working in [category]", "show me formulas for talking-head ads", "what scripts work in my vertical", or wants category-level pattern discovery before committing to a single ad. Pass the returned formula id to generate_adscript with source_type="formula" for synthesis. When choosing among results: prioritise (1) avg_active_days as primary proof, (2) marketing_angle alignment with the brand's buyer tension, (3) source_ad_count for cluster robustness, (4) confidence_score as tiebreaker. Do NOT use when the user names a specific ad — decode that ad with decode_ad. Do NOT use for sentence-level transcript fidelity — formulas abstract the structure, not exact copy.ConnectorNo auth
- Fetch the current HEAD of a report by id. `format=markdown` returns the rendered body, `format=json` returns the full structured payload (sections + citations + report-type-specific data), `format=preview` returns abstract-only. Authors see any of their own reports; non-authors only get `preview` of listed reports and need the report's required tier for full bodies. Sample-tier non-authors are downgraded to preview regardless of input. For an archived prior version use `get_report_version`, not this tool.ConnectorNo auth
- Render a structured research brief into a professionally-styled Word document — a branded masthead-first page (Valuein letterhead: brand rule, wordmark, 'EQUITY RESEARCH' kicker + date, then the ticker eyebrow, the title as hero, and the named analyst's byline), the body (abstract, optional snapshot table with figures in mono, markdown sections incl. GFM tables, and a citations table with clickable SEC EDGAR links), with a running footer (ticker, 'Built on Valuein · valuein.biz', page number, a single disclosure line) repeated on every page. No embedded charts in v1; pair with `generate_dcf_xlsx` / `generate_comps_xlsx` for visuals the analyst pastes in. SERVER-TRUST: prose, snapshot rows, and citations are rendered as-supplied and are NOT verified by Valuein, so the brief carries a visible 'figures supplied by caller, not verified by Valuein' watermark (response `verification.status` = 'unverified'). Resolve each citation via `verify_fact_lineage` before publishing. Consumes the same `sections` + `citations` shape `create_report` emits, so the typical flow is two tool calls: `create_report` → `generate_research_brief_docx`. Tier: pro+.ConnectorNo auth
- Fetch full patent details by patent number and jurisdiction. Read-only. No side effects. Idempotent. patent_number: Patent number in EPODOC format e.g. EP1000000 for European, CN120586032 for Chinese, JP2020123456 for Japanese, WO2020123456 for PCT, US10000000 for US. Required. jurisdiction: Optional hint — one of EP, CN, JP, KR, US, WO, etc. Default EP. The tool normalises the patent number automatically; passing CN120586032 with jurisdiction EP is valid. Returns title, abstract, inventors, assignees, filing date, claims summary, and citation count. Use this when you have a specific patent number. Use legal_search_patents_by_keyword instead when you only have keywords and need to find patents. Verified source: EPO OPS. 24-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="legal_fetch_patent_by_number", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".ConnectorNo auth
- Submit a new version of an existing document. The earlier version is not replaced: it keeps its own identifier, so an existing citation of it still resolves to the exact text it referred to. The previous version's chunks will be marked as not-latest. Omit `categories`, `keywords`, or `language` to inherit each independently from the previous version; pass a value to override. Content is file-only: provide a base64-encoded ZIP archive (content_archive_base64) OR a content_ref from an out-of-band upload — exactly one. A ZIP may hold a single PDF, markdown + figures, or multifile LaTeX. Inline text is no longer accepted. For content above ~10 KB, prefer create_upload_url → PUT the file to the returned URL → pass the returned file_id as content_ref (avoids base64 token bloat). content_archive_base64 and content_ref are mutually exclusive — provide exactly one. Limits: title ≤5,000 chars; abstract ≤50,000 chars; archive ≤50 MB; keywords ≤50 items × ≤100 chars each. Set dry_run=true to validate without committing: no document is created, nothing is queued, no credits are charged; the response shows what would be saved and the estimated cost.ConnectorAPI key
- Check whether a single scholarly work has been retracted, corrected, or had an expression of concern raised. Use when the user asks 'has this paper been retracted?' or wants to verify a paper's standing before citing it (clinical, regulatory, evidence-synthesis contexts). For multi-paper bibliography audits (clinical guidelines, systematic reviews), loop one call per identifier — the tool intentionally rejects batch input to keep retraction-status results unambiguous per work. Sourced from Crossref `updated-by` (which mirrors Retraction Watch). Resolves DOI/PMID/PMCID/arXiv/ADS inputs to a DOI before lookup; ISBN inputs always return doi=null and reason='no_doi' since books are not in the retraction graph. arXiv inputs check the linked published-journal DOI when arXiv records one; a preprint without one returns doi=null and reason='no_doi' (preprints are outside the Crossref retraction graph — arXiv marks withdrawals on the abstract page instead). Single identifier per call — does NOT accept comma/newline batches; loop one call per identifier for multiple papers. Returns: { doi, resolvedFrom?, reason?, result } where result has isRetracted, hasCorrections, hasConcern (booleans), notices (array of {type, label, doi, date, source} where type is a raw Crossref update type such as 'retraction', 'correction', 'erratum' or 'expression_of_concern'), and title; result is null when no DOI could be resolved and reason explains why ('no_doi'). No sibling tool overlaps this — resolveIdentifier returns metadata but not retraction status. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier; Crossref is queried server-side with its own caching.ConnectorNo auth
- START HERE for any scientific, biomedical, clinical, or AI/ML claim. This is the default entry point to Zetesis. It returns the source catalog and the grading rubric so you can work out which evidence dimensions actually matter for THIS claim and what to search for, then you call zetesis_evidence with the queries you chose and grade what comes back. Use it whenever a claim needs assessing, screening, sanity-checking, or due diligence: a research claim, a study, a paper, an abstract, a preprint, a grant, or a pitch. Also use it before citing a paper or statistic in your own answer. Returns three things: the source catalog (where to look), the diligence apparatus for this claim's class (what to ask, and the failure patterns that have caught comparable claims before), and the scoping rubric. Runs no model, costs nothing, and returns immediately. This two-step route is preferred over evaluate_claim on every axis that matters: it is instant rather than minutes, it reads the sources at full depth in your context rather than at abstract level, and it supports evaluating a claim as it stood in an earlier year. Args: claim: the claim to evaluate, in one or two sentences. context: optional background (stage, field, the decision at hand). as_of: optional year. Set it to evaluate the claim as it stood at the end of that year, grading how it was derived at the time rather than how it turned out. claim_class: optional override for the routed claim class. The response lists every class; pass one back if the routing picked wrongly, or if nothing routed.ConnectorNo auth
- Record your answer's full source decision set — what you cited and what you excluded, each with a reason and its provenance — as a verifiable, auditable citation record. Call this ONCE at the very end of a response that drew on sources, with your full decision set: - every source you CITED (credited in the answer), and - every source you retrieved/considered but EXCLUDED, each with a short reason. Report only sources you actually used — never invent references. Fire-and-forget: it records the decisions and does not change your answer. Use it for: - Fact-checking / reducing hallucinations: works with search_literature (every cited source must trace to a real retrieved record), citation_graph (screen the literature by citation topology, then log which edges you kept vs. dropped and why), and bibliography (references built from stored metadata, not memory). Recording each decision — then reviewing it with citation_report before you finalize — surfaces fabricated, misattributed, or unsupported citations. - Provenance: `source` records WHERE each source came from — scite_mcp (retrieved via scite), web_search, user_supplied, or other — so a reader can tell verified retrievals from unverified ones. - Systematic review / PRISMA screening: the `excluded` items with `reason_code` and `stage` are the screened-out log with reasons at each stage (title/abstract vs full text) that PRISMA requires; the `cited` items are the included studies. - Regulatory / evidence submissions: a reproducible, per-source trail of what was included, what was excluded, and why — auditable straight from the recorded decisions. Each citations item: - source_ref: the DOI (preferred) or, for non-scite sources, a URL/reference string. - decision: "cited" (included/credited) or "excluded" (screened out). - source: provenance — "scite_mcp", "web_search", "user_supplied", or "other". - source_detail: name the source when source is "other" (e.g. "arxiv", "google scholar"). - reason_code: short reason — for excluded: off_topic, retracted, contradicted, duplicate, low_quality, superseded, out_of_scope; for cited: e.g. supports, relevant. - reason: optional free-text note explaining the decision. - stage: optional PRISMA screening stage — "title_abstract" or "full_text". Returns JSON: recorded_cited, recorded_excluded, skipped (malformed items dropped), mcp_session_id, and the accepted decisions grouped as cited[] and excluded[] (each item with source_ref, source, source_detail, reason_code, reason, stage) so a client can render a used/rejected citation panel.ConnectorNo auth
- Look up MITRE CWE (Common Weakness Enumeration) catalog record from research view 1000. Default response is SLIM (first 3 mitigations, first 3 examples; extended_description is null) — pass include='full' for the verbose record (full mitigations + examples lists, populated extended_description). Returns description, abstract type (Pillar/Class/Base/Variant/Compound), status (Stable/Draft/Incomplete/Deprecated), exploit likelihood, recommended mitigations, observed example CVEs, parent_cwe (walk up the hierarchy), child_cwes (drill down to more specific weaknesses), and cve_count (LOWER BOUND — counts only CVEs whose primary CWE matches; CVEs with multiple CWEs may not be counted). Use after cve_lookup or kev_detail to understand the underlying weakness category; chain with cve_search(cwe_id=...) to enumerate all matching CVEs. Returns 404 when the CWE is not in research view 1000. Free: 30/hr, Pro: 500/hr. Returns {cwe_id, name, description, extended_description (null on slim, populated on include='full'), abstract_type, status, likelihood, mitigations (first 3 by default), total_mitigations, examples (first 3 by default), total_examples, parent_cwe, child_cwes, cve_count, updated_at, verdict, next_calls}.ConnectorNo auth
- Search scientific literature and read full-text content from peer-reviewed papers. Use `dois` (preferred) or `titles` with targeted `term` queries to extract full-text passages from specific papers. Each call returns up to 5 relevant excerpts (~500 chars each) — vary search terms across calls to read through a paper section by section. **IMPORTANT — keep `limit` small.** Use `limit: 10-50` with `offset` for pagination. Large limits with full citations and excerpts produce very large payloads that consume significant LLM context. **Calling with no parameters browses the corpus** (210M+ papers, relevance-sorted). This is allowed for broad exploration but rarely what you want — pass `term`, `dois`, `titles`, or other filters for targeted results. **What This Tool Returns:** - Paper metadata: title, authors (first 3), abstract, DOI, journal, year, volume, issue, page - `fulltextExcerpts`: up to 5 passages (~500 chars) from the paper matching your query (OA only) - `access`: resolved access link with source, type (open/institutional/purchase), content type, and pricing - `citations`: Smart Citation statements — actual quoted text from citing papers, classified as supporting/contrasting/mentioning/unclassified (unclassified = statement present but classifier hasn't assigned a type) - `tally`: citation metrics (total, supporting, contrasting, mentioning, citing publications) - `editorialNotices`: editorial notices (retraction, correction, concern, erratum), each with status, noticeDoi, date - `isOa`, `oaStatus`, `license`: open access information **Fetching Paper Metadata (no search term needed):** Pass `dois` or `titles` WITHOUT a `term` to retrieve metadata for specific papers. Example: `dois: ["10.1038/s41586-020-2012-7"]` **Full-Text Excerpts:** For OA papers, `fulltextExcerpts` contains passages matching your query. If empty, the full text is not indexed or terms didn't match — use the `access` field for the best link to the PDF or full text. **Smart Citations ARE Full-Text Evidence:** - `snippet`: exact sentence/paragraph from the citing paper's full text - `type`: classification (supporting, contrasting, mentioning, unclassified) - `section`: paper section (Introduction, Methods, Results, Discussion) - `sourceDoi`: paper containing this snippet; `targetDoi`: paper being cited **Search Capabilities:** - Boolean operators: AND, OR, NOT - Phrase search: "exact phrase" - Proximity: "term1 term2"~5 - Field filters: title, abstract, author, journal, year, affiliation - Citation filters: supporting_from/to, contrasting_from/to, mentioning_from/to - Editorial filters: has_retraction, has_concern, has_correction, has_erratum **Parameters:** - `term`: cross-field search query (optional when `dois`/`titles` provided) - `dois`: array of DOIs to filter to specific papers - `titles`: array of titles to filter (use when DOIs unavailable) - `limit`: max results (default: 10, max: 1000) - `offset`: pagination offset - Plus 20+ filter parameters (see schema) **Response Format:** ```json { "hits": [{ "doi": "10.1234/example", "title": "Paper Title", "authors": [{"authorName": "Jane Smith"}], "abstract": "Full abstract text...", "year": 2023, "journal": "Nature", "tally": {"supporting": 32, "contrasting": 8, "mentioning": 5}, "fulltextExcerpts": ["Relevant passage..."], "access": {"url": "https://...", "accessType": "open", "contentType": "pdf"}, "citations": [{"snippet": "These findings...", "type": "supporting", "section": "Results"}], "editorialNotices": [{"status": "retracted", "noticeDoi": "10.1234/notice", "date": "2021"}] }] } ```ConnectorNo auth
- Search academic references (journal articles, book chapters, theses, books, reports) by keyword and metadata. `keyword` is a single substring match over title + abstract, so search ONE term per call (combined terms like 'pèlerinage Mecque' miss results). References are multilingual: try French and English title/abstract keywords when relevant; metadata/filter values such as `reference_type` and `language` use French labels. Results include a short abstract snippet — use get_reference for the full abstract and bibliographic detail.ConnectorNo auth
- Fetch complete Europe PMC records — including the full, untruncated abstract — for records addressed by `source` plus `epmcId`. Pairs with `pubmed_europepmc_search`, which returns bounded `abstractSnippet` values and flags cut ones with `abstractTruncated: true`; pass those hits' `source` and `epmcId` here to read the whole abstract. This is the retrieval path for preprint (`PPR`), patent (`PAT`), and Agricola (`AGR`) records, which frequently carry no PMID and no DOI, so `pubmed_fetch_articles` and `pubmed_fetch_fulltext` cannot address them. Up to 25 records per call.ConnectorNo auth
- Self-Inspect. Express a thought, or describe the task you are working on, and you always get back ONE metathought: a short abstract question that makes you inspect your own task and assumptions before continuing. Use it whenever you want a self-check: after forming a hypothesis, before committing to an answer, when a long chain feels like it has drifted, when you notice you are agreeing to please, or when you are about to assert something from memory. There is no failure case: it always returns a metathought to question yourself with, selected by a transparent heuristic over an open CSV (no LLM). Keyless and free. DO NOT call for factual lookups or as a substitute for doing the task. Absorb the question and act on it; do not echo it verbatim to the user.ConnectorNo auth
- Answer a RULE-LEVEL question directly from compiled law: thresholds and day counts, WITHHOLDING TAX rates on royalty and fees for technical services (treaty and domestic), the 1961→2025 Income-tax Act section renumbering (s.195→s.393(2), s.115A→s.207, s.90→s.159, s.206AA→s.397(2)), tests and their elements, what a named case held. Ask in plain language — 'what is the India–US royalty WHT rate' (15%, not the widely-repeated 10%), 'what replaced section 195', 'is software payment royalty after Engineering Analysis', 'is a TRC sufficient after Tiger Global', 'what does make available mean'. Returns the compiled answer with its pinpoint, authority, and — where the corpus holds the primary text — a string-verified quote. Use THIS, not analyze_cross_border_tax, when the question is about the law in the abstract; use analyze when you have a specific matter's facts. Outside compiled topics it refuses and lists what can be asked.ConnectorNo auth