"Methods for Analyzing Notes, Journal Articles, and Brainstorming Ideas" matching MCP tools:

• pubmed_lookup_citation

  pubmed-mcp-server

  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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• pubmed_convert_ids

  pubmed-mcp-server

  Convert between article identifiers (DOI, PMID, PMCID). Accepts up to 50 IDs of a single type per request. Only resolves articles indexed in PubMed Central — for articles not in PMC, use pubmed_search_articles instead.

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• get_full_text

  Pubmed

  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_journal

  crossref

  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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• get_stock_news

  Stocklake — AI Stock Intelligence

  AI-analysed news for a stock, newest first. Only returns articles processed by our AI pipeline (sentiment, flag score, summary). - days: look-back window in days (default 30, max 30) - limit: max articles returned (default 10, max 10) - status: "ok" = articles returned | "empty" = no news in window - Per article: title, published_at, ai_sentiment, ai_flag_score (0-10), ai_summary (full text), ai_confidence (0-10) Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• fetch_notes

  Sweeppea MCP

  Get all notes for your account. Notes are automatically decrypted and returned in reverse chronological order. Use them internally for tool chaining but present only human-readable information (titles, content, dates). # fetch_notes ## When to use Get all notes for your account. Notes are automatically decrypted and returned in reverse chronological order. Use them internally for tool chaining but present only human-readable information (titles, content, dates).

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• get_summary

  Most Recommended Books

  Return MRB's one-page summary of a book: the overview plus chapter-by-chapter notes (~435 titles available). Use this when a user asks 'summarize Atomic Habits', 'what is Sapiens about?', or wants the key ideas without reading the book. Set full_text=true only when the user explicitly wants the complete chapter notes — they can be very long.

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• create_note_event

  STAS Running Coach for Claude

  Create or update NOTE events in Intervals. dry_run is required: false writes the note, true previews only. Send category=NOTE and external_id=note:YYYY-MM-DD:<slug>. Use all-day local times for normal notes, keep description short, and omit type, moving_time, icu_training_load, and workout_doc. For weekly review notes or other notes that apply to the whole week, send for_week=true; omit it or use false for ordinary notes. Do not create a seven-day date range for weekly notes; keep one all-day anchor date and use for_week=true.

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• get_discovery_ideas

  Stocklake — AI Stock Intelligence

  AI-screened stock ideas actively flagged by the Stocklake pipeline. These are stocks the pipeline's AI agents have identified as worth attention — sourced from news analysis, sector screening, and sentiment signals. Parameters: - direction: "LONG" | "SHORT" | "BOTH" (default: all) - min_conviction: minimum conviction score 0-10 (default 7) - min_flag_score: minimum flag score 0-10 (default 8; 9+ = high conviction) - source: filter by signal source — "news" | "screener" | "sentiment" (default: all) - limit: max results to return (default 25, max 50) Returns: - count: number of ideas returned - ideas[]: each with symbol, direction, conviction (0-10), confidence (0-10), flag_score (0-10), source, rationale, expires - Note: ideas expire daily — active ideas represent the pipeline's current view. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• analyze_qbo_journal_cleanup

  HelloBooks AI Agents

  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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• analyze_qbo_journal_cleanup

  HelloBooks AI Agents MCP Server

  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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• pubmed_find_related

  pubmed-mcp-server

  Find articles related to a source article — similar content (similar), articles citing this one (cited_by), or articles this one cites (references). Uses NCBI ELink as the primary source; falls back to Europe PMC then OpenAlex when NCBI is unavailable.

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• search_works

  crossref

  Search academic papers, books, and datasets via Crossref. Beyond keyword search you can SORT by citation count or publication date and FILTER by date range, work type, and author — e.g. "most-cited papers on transformers" (sort=citations), "papers on LLMs since 2024" (from_date=2024-01-01), "recent journal articles by an author". Returns titles, authors, journal, DOIs, citation counts, and dates.

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• get_abstract

  Pubmed

  Full abstract text for one PubMed article by ID. Returns the abstract with structured sections (background, methods, results, conclusions) when the journal published it that way, otherwise the unstructured abstract. Use when summarizing a single paper or answering "what does paper X actually say". For batch citation metadata use get_summary; for finding papers use search_pubmed.

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• batch_geolocate

  iplookup

  Look up locations for up to 100 IP addresses at once. Returns geolocation and ISP data in the same order as input. Use for analyzing multiple IPs efficiently.

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• get_news_articles

  TensorFeed

  Get the latest AI news articles aggregated from 12+ sources (Anthropic, OpenAI, Google, HuggingFace, TechCrunch, The Verge, Hacker News, etc). Polled every 10 min, deduplicated, sanitized for prompt injection. Returns up to 200 articles with title, snippet, source, and publishedAt.

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• ftg_business_ideas

  mcp-knowledge

  Return vetted, automation-scored business ideas from the FTG idea bank — each with an autonomy score, monetization model and conservative/median/optimistic MRR projections. When to use this tool: an agent or founder wants ranked, buildable business ideas. Input: optional category and limit.

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• content_evergreen_score_analyzer

  mcp-knowledge

  Evaluates content evergreen potential for CMOs by analyzing historical traffic patterns and backlink authority. Takes a content URL and optional time range, returns an evergreen score (0-100), traffic trend analysis, and backlink profile. Ideal for content strategy planning, SEO optimization, and identifying high-value evergreen assets. Uses Wayback Machine and Common Crawl public APIs.

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• talent_litigation_exposure

  mcp-knowledge

  Estimates litigation exposure risk for CHROs by analyzing past employee lawsuits, settlement amounts, and industry benchmarks. Inputs include company location, industry code, and employee count range. Returns exposure score, average settlement amounts, lawsuit frequency trends, and risk factors. Ideal for legal risk assessment, HR strategy planning, and board-level reporting. Pass async:true to avoid timeout.

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• syndicated_loan_covenant_breach_alert

  mcp-knowledge

  Monitors syndicated loan covenants for potential breaches by analyzing Tradeweb market data. Designed for CFOs to proactively identify financial compliance risks in loan agreements. Accepts loan identifiers, covenant thresholds, and reporting period as inputs. Returns structured breach alerts with market context and severity indicators.

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