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240,604 tools. Last updated 2026-06-27 09:37

"A guide to writing a paper for an SCI-indexed journal" matching MCP tools:

  • Purpose: Track-A (LLM-driven) paper-trading judgement log. When to call: inspect LLM-generated reasoning and trade calls. Prerequisites: none. Next steps: get_latest_decisions to compare with Track B. Caveats: paper-trading only. Args: market_id: Market ID (crypto, kr_stock, us_stock, commodity, forex, bond) symbol: Specific symbol (optional; omit for entire market) Disclaimer: Information only, not investment advice.
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  • Look up PubMed IDs from partial bibliographic citations. Useful when you have a reference (journal, year, volume, page, author) and need the PMID — deterministic citation matching, more reliable than free-text search for structured references. Each citation must include at least journal or year (ECitMatch primary-keys on journal+volume+page; author-only or volume-only inputs guarantee no match); more fields = better match accuracy.
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  • Fetch the FULL TEXT of a biomedical paper from PubMed Central (the open-access subset) by PubMed ID. PREFER OVER get_abstract when you need methods/results/discussion, not just the abstract — "read the full paper", "what methods did <PMID> use", "extract details from the paper". Resolves the PMID to its PMC id and returns the article body text (capped ~40k chars). Only open-access articles are in PMC — returns has_full_text:false (use get_abstract) otherwise.
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  • Get the 5 most recent works from a journal by ISSN (e.g., "2041-1723"). Returns titles, authors, DOIs, and publication dates.
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  • Create a third-party LEAD-GENERATION page about a business (NOT a site for that business itself). Use this when the goal is to drive qualified search traffic to someone else's business — affiliate pages, review/guide pages, niche directories. The page is branded as an outside guide (e.g. "Best Roofers in San Diego"), refers to the business in the third person, and routes CTAs to the business's existing website. Differences from create_site: - Slug + page brand are SEO-vanity (e.g. "best-roofers-sandiego"), not the candidate's brand name. - Voice is third-party guide/reviewer — never first person. - Primary CTA is "visit their website"; phone/email demoted. - No specific pricing quoted; differentiators emphasized. - Locality is judged by category, not just address (IT/SaaS/agency stays category-wide even when a city is on file). Pass a business candidate object from search_businesses — that business is the one being PROMOTED. Requires authentication via API key (Bearer token). Generate an API key at webzum.com/dashboard/account-settings. The page generation happens in the background. Use get_site_status to check progress. Returns the businessId (a vanity slug) which can be used to access the page at /build/{businessId}.
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  • "How many references does paper [DOI] have" / "how big is the bibliography of [paper]" — outgoing reference count for a DOI. Fast version of `references` when you only need the number.
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    MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.
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  • Verify a claimed citation against the resolved record at its identifier. Detects the dominant AI-driven fabrication pattern documented by Topaz et al. (Lancet 2026): a real, resolvable identifier (DOI / PMID / PMCID / arXiv / etc.) paired with a title that does NOT correspond to the paper at that identifier. Use when the user pastes a citation and asks 'is this real?' or 'check this DOI' — most fabricated citations resolve cleanly under doi.org but their cited title and the resolved title disagree. Single citation per call. Required: `title` plus exactly one identifier (doi, pmid, pmcid, isbn, arxiv, issn, ads, or whoIrisUrl). Optional refinements: author (first-author family name), year, container (journal). Set `screenWithLlm: true` to invoke the Stage 3 LLM screen on low-confidence mismatches (catches informal-abbreviation false positives); LLM access is gated to authenticated first-party keys and paid RapidAPI tiers — anonymous callers get 400 LLM_SCREEN_FORBIDDEN. Returns: { verdict: 'matched' | 'mismatch' | 'not_found' | 'ambiguous', confidence: 'high' | 'medium' | 'low', matched: <resolved record or null>, mismatches: [{field, claimed, resolved, similarity}], candidates: [{item, registries, score}] (when title-search ran), _provenance: {stages_run, resolved_via, registries_searched, llm_screen} }. Verdict semantics: 'matched' = claim agrees with resolved record; 'mismatch' = identifier resolves but title does not match (Topaz fabrication pattern); 'ambiguous' = identifier resolves to one paper but the claimed title matches a DIFFERENT paper found via title-search (CITADEL 'citation error' subtype — wrong identifier for a real paper); 'not_found' = neither the identifier nor the title resolves anywhere. No sibling tool overlaps: resolveIdentifier returns metadata for a known-good identifier; verifyCitation is the only tool that cross-checks claimed title vs resolved metadata. Read-only and idempotent — safe to retry. Works anonymously for the non-LLM path; the Stage 3 LLM screen requires authentication — set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) or use a paid RapidAPI tier. SCHOLAR_API_KEY also raises your rate limit.
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  • Search Google Scholar for academic papers, citations, and scholarly articles. Returns results with titles, authors, publication info, citation counts, and links to PDFs. Use cites parameter to find papers citing a specific work, or cluster to find all versions of a paper. For US court opinions and case law, use google_scholar_cases instead.
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  • Get today's quantum computing papers from arXiv — no parameters needed. Use when the user asks "what's new in quantum computing?" or wants a daily paper briefing. Returns the most recent day's papers with title, authors, date, AI-generated hook (one-line summary), and tags. For date-range or topic-filtered search, use searchPapers instead. Use getPaperDetails for full abstract and analysis of a specific paper.
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  • 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.
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  • Place a paper spot order. coinId is a coin UCID, NOT a ticker. orderType market/limit/stop. limitPrice required for limit & stop; stopPrice required for stop. idempotencyKey is REQUIRED and unique per intent (reuse replays the original result — retry a timed-out call with the SAME key; it will never double-execute). Requires the trade:spot scope. CONFIRM with the user before calling. Paper trading only — virtual funds (50,000 mUSD). Not financial advice. Paper fills apply a disclosed execution cost folded into realized PnL: spot/futures pay a taker fee (spot market orders also pay half-spread + slippage); PM fills at the ask with size-based slippage and a Polymarket-shaped taker fee, with entryProbability kept at the mid for calibration. See the executionModel in quote/trade results — a rehearsal cost, not an exchange fill guarantee.
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  • Given the file paths an agent is about to change (and optionally a subset being deleted), return the conventions, documentation gaps, and existing/related docs whose evidence overlaps those paths, plus a net-new/undocumented analysis and any removal candidates. Read-only; no side effects. Returns a Markdown report. Call this BEFORE writing code so doc updates land in the same PR; then use propose_doc_update to write a doc, or propose_doc_removal for an orphaned one.
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  • Seam linter: flag drivers writing into a namespace they do not own (read-only). Always checks the structural invariant that a driver's declared ``namespace`` matches its canonical key (a corrupt-IR tripwire). When ``ownership`` (namespace -> owner-id) is supplied, also flags writer-drivers landing in a namespace the manifest never declared an owner for. Never mutates; never compiles.
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  • Look up a single paper by its DOI. Args: doi: The DOI of the paper (e.g. "10.1038/s41586-024-07386-0"). api_key: Optional: Your Stripe subscription ID for paid access. Get one at https://bgpt.pro/mcp Returns: Paper with title, DOI, Raw Data, methods, results, quality scores, and 25+ metadata fields — or an error if not found. Costs $0.02 if found, free if not.
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  • Add a new slide to an existing presentation. Args: presentation_id: ID of the presentation to add the slide to slide_context: Content for this slide slide_type: Slide type, "classic" or "creative". Defaults to "classic". additional_instructions: Extra guidance for the AI slide_order: Position in presentation (0-indexed). Omit to append at end. Returns a generation_id to poll for completion.
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  • Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Load Lenny Zeltser's complete cybersecurity-writing rating toolkit: all 7 sheets, scoring policy, scoring playbook, and cross-references to the writing guidelines. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Look up a Wikidata entity by an external identifier such as a DOI, PubMed ID, ORCID iD, or OpenAlex ID. Returns match=<entity> on success, match=null when not found, and match=null with multipleMatches populated when a Wikidata data integrity issue causes more than one entity to claim the same external ID. Common cross-server join use cases: CrossRef DOI → Wikidata paper QID (P356), PubMed PMID → Wikidata paper QID (P698), ORCID → author QID (P496), OpenAlex ID → entity QID (P10283). Known value normalization is applied automatically: DOIs are uppercased, PMID prefixes stripped, ORCID hyphens normalized.
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  • Scan a QuickBooks Online "Journal Entries" CSV export for cleanup issues — unbalanced journals (debits ≠ credits, with severity by deviation), duplicate journals (same date + same totals, likely posted twice), and schema problems (invalid dates, malformed amounts, missing accounts, missing journal numbers). Input is the raw CSV content the user pastes after exporting from QBO via Reports → Accountant → Journal → Export. Max 5,000 rows; max 5 MB. Returns a structured flag list with severity (high/medium/low), a roll-up summary by category and severity, parse diagnostics (column mapping + unmapped columns), and a shareable URL at agents.hellobooks.ai/r/{slug} (7-day TTL) that renders a branded analysis page suitable for sending to a CA or bookkeeper. Use this when a user pastes QBO journal data, asks "check my books", "find issues in my QBO journal", or "what is wrong with my journal entries". Each flag includes a `fixableInHellobooks` boolean — true means HelloBooks can resolve it automatically in the paid product.
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