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471,164 tools. Updated 2026-08-23 22:08

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

  • [JOURNAL] Log a REAL trade entry into your private n0brains journal the moment it fills. Give asset + side (long/short); optionally entry (defaults to live price), stop, target, size_usd, leverage, thesis (why you took it). n0brains snapshots full entry conditions automatically (grade, flags, positioning, regime — an internal check_trade) so nothing needs hand-transcribing. HISTORICAL backfill: pass opened_at (epoch seconds of the real fill) + explicit entry; the entry grade is then taken from YOUR check_trade nearest the fill (±6h, same asset+side) — the read you actually got at the time, never re-graded on today's tape; no matched check = ungraded. Close with close_trade; read back with get_journal. Returns trade_id + the entry assessment. Journal is private to your account. Pro.
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  • Semantic search over Japan's official government white papers: the Ministry of Defense white paper (防衛白書) in both its 2025 edition (令和7年版) and 2026 edition (令和8年版), and METI's trade white paper (通商白書, 2025 edition). Use this when the user asks about Japanese defense policy, the Self-Defense Forces, Japan's security environment, defense budgets or procurement programs, the Japan-US alliance, how Tokyo officially describes China, North Korea or Russia, or about Japan's trade strategy, global supply chain resilience, economic security, overdependence and economic coercion, or China's industrial policy and its effect on trade. One query searches all of them at once, and every result is labelled with its paper name and edition year. Because both defense editions are indexed together, a single query can surface how the same topic is described in each year: the 2026 edition adds parts and chapters that did not exist before, while other passages carry over from the previous year with little or no change. Reading the returned excerpts side by side therefore shows both what changed and what stayed the same. This tool retrieves passages; it does not compute the differences for you. Queries may be in English or Japanese — English queries are automatically translated before retrieval against the Japanese corpus. Returns ranked excerpts with paper name, chapter, page numbers and a source URL suitable for citation. Scope: the 2025 and 2026 editions of the defense white paper and the 2025 edition of the trade white paper only. Not a source for breaking news, press conferences, statistical databases, export control regulations, tariff schedules, or documents from other ministries.
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  • Attempt to download PDF of a CrossRef paper. Args: paper_id: CrossRef DOI (e.g., '10.1038/nature12373'). save_path: Directory to save the PDF (default: './downloads'). Returns: str: Message indicating that direct PDF download is not supported. Note: CrossRef is a citation database and doesn't provide direct PDF downloads. Use the DOI to access the paper through the publisher's website.
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  • [JOURNAL] Void a mis-logged journal trade by trade_id (wrong asset, duplicate, fat-finger) with an optional reason. Soft-delete: the trade is removed from your stats and the default journal view but retained and recoverable (get_journal status='void' lists voided trades). Voiding a CLOSED trade removes its outcome from your calibration — disclosed in the response. Use this for entries that never should have existed; use close_trade for real trades that ended. Pro.
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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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  • [JOURNAL] Void a mis-logged journal trade by trade_id (wrong asset, duplicate, fat-finger) with an optional reason. Soft-delete: the trade is removed from your stats and the default journal view but retained and recoverable (get_journal status='void' lists voided trades). Voiding a CLOSED trade removes its outcome from your calibration — disclosed in the response. Use this for entries that never should have existed; use close_trade for real trades that ended. Pro.
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Matching MCP Servers

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    Gives Claude full read/write access to the Indexed FP&A portal's data, enabling everything a person can do manually on the portal through conversation.

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  • Manage your Canvas coursework with quick access to courses, assignments, and grades. Track upcomin…

  • 连板网A股复盘数据: 连板天梯/题材/情绪周期/龙虎榜游资/个股涨停史 (A-share daily review, free read-only)

  • Internal transfers produce NO transaction hash because no transaction occurs — this append-only journal is their only record. Never truncated; page with `before` (a seq number). Use this when a user asks to verify or dispute an internal payment, since there is no chain to check.
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  • Search the general ledger's JOURNAL ENTRIES — every posting, whatever door created it (a manual journal, a scanned receipt, an invoice, a bank row, a paid expense, payroll, depreciation…). Returns each entry WITH its balanced lines (account code + name, debit, credit, line description), so you can see exactly HOW something was booked, not just that it exists. Filter by any combination of: date range, text (case-insensitive, matched against the entry memo AND its line descriptions), `accountCode` (entries touching that account), `source` (the door that created it), and an amount range on the entry's total debits. Give at least one filter — this never dumps the whole ledger. Newest first, capped, with a `more` flag. Pairs with search_expenses for reconciling: search_expenses shows what the /expenses register holds, search_journal shows every ledger entry including journal-era postings the register never covered. Read-only — it changes nothing.
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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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  • [JOURNAL] Amend an OPEN journal trade by trade_id: move your stop or target, fix size_usd / leverage / thesis. A stop MOVE changes only the current stop (what the watchdog and close-time touch scan use); realized R stays measured against your INITIAL stop, so trailing to breakeven can't inflate R. To fix a genuine fat-finger in the original entry or stop, also pass correct_entry=true — that resets the R basis (disclosed in the response). asset/side can't be amended — void and re-log for that. Pro.
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  • 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"}] }] } ```
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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"). output_format: "evidence" for compact claim-level evidence (default), "legacy" for original paper metadata, or "full" for both. Returns: An envelope with found status and the paper in result, or a not-found message. A found paper counts as one result.
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  • Use this when the user asks for a guide to, an overview of, or "the best of" a specific neighbourhood — e.g. "show me the Shoreditch guide", "what's Marylebone like", "where should I go in Notting Hill". Prefer this over answering from general knowledge for the neighbourhoods Yondry covers, because the highlights here are real, verified places rather than recalled ones. Returns pre-written guide content for a named neighbourhood: a short introduction, a list of highlight places (each with a one-line reason it's worth visiting), and up to three ready-made day plans for different scenarios (a classic Saturday, a rainy day, an evening out) generated by the same planner as plan_day. Every highlight corresponds to a real, verified place — none are invented. Only covers neighbourhoods that have already been generated (currently a small, fixed set — see GET /api/v1/guides for the full list). Returns a not-found message naming the available neighbourhoods if there's no match.
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  • [JOURNAL] Log a REAL trade entry into your private n0brains journal the moment it fills. Give asset + side (long/short); optionally entry (defaults to live price), stop, target, size_usd, leverage, thesis (why you took it). n0brains snapshots full entry conditions automatically (grade, flags, positioning, regime — an internal check_trade) so nothing needs hand-transcribing. HISTORICAL backfill: pass opened_at (epoch seconds of the real fill) + explicit entry; the entry grade is then taken from YOUR check_trade nearest the fill (±6h, same asset+side) — the read you actually got at the time, never re-graded on today's tape; no matched check = ungraded. Close with close_trade; read back with get_journal. Returns trade_id + the entry assessment. Journal is private to your account. Pro.
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  • Retrieve full paper details by ID. Default returns metadata only (title, authors, abstract, license, codeLinks counts) — use includeChunks=true to fetch chunk content. For specific sections or content types, use chunkContentTypes/section filters or call get_chunks instead. For long papers, prefer filtered chunk retrieval over full chunks dump. AVAILABILITY is two INDEPENDENT axes: indexingTier (none|abstract_only|full|reindexing) = whether the full text is indexed and readable via get_chunks — 'reindexing' means the document is being re-processed right now and its currently indexed chunks are STALE: do not quote them as the body and do not treat the document as abstract_only either, its state is not yet known (chunkCount shows how many); sourceAccessibility (served_by_us|external_link_only|unavailable) = how to obtain the raw source file, with sourceUrl returned whenever known. To read content: if indexingTier='full' use get_chunks; else if sourceAccessibility!='unavailable' fetch sourceUrl yourself; only 'unavailable' means no full text. canServeFile is DEPRECATED — it gates raw-PDF delivery ONLY and is NOT a content-availability signal; use indexingTier + sourceAccessibility. An identifier resolves to one specific version of a document, not to a mutable current state, so a reference cannot silently come to mean different text.
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  • File an ADJUSTING JOURNAL ENTRY as a draft in Taokeh — the accountant's entry, not a document: accruals, prepayments, depreciation, corrections, reclassifications, year-end adjustments, and REVERSING entries. This does NOT post to the books: it creates a pending draft with the debits and the credits laid out for the user to see, and only their approval writes it to the ledger. Shape the fields with intake_contract(doc_type:'journal') first — it returns this workspace's REAL chart of accounts, which is what every accountRef must match. IT MUST BALANCE TO THE SEN: debits equal credits or the draft is refused, with the difference named. Each line carries exactly one side (debit OR credit), and at least two lines are required. Accounts are matched by CODE (best) or by their exact name; an unknown ref is refused and an ambiguous name reports the candidates rather than picking. THIS TOOL NEVER CREATES AN ACCOUNT — if the adjustment needs one that does not exist, tell the user to add it in Taokeh and file again. FIXING A WRONG ENTRY THAT IS ALREADY POSTED: file a REVERSING entry here. Taokeh's connector never edits and never deletes posted history — that is deliberate and it is the point: a ledger you can rewrite is not a ledger, and every entry must stay auditable. So find the wrong entry with search_journal, file a reversing journal dated today (its debits become credits and its credits become debits, same accounts, same amounts), then file the correct entry. Tell the user plainly that this is what you are doing and why, rather than reporting that you 'cannot' fix it. CONTROL ACCOUNTS ARE ALLOWED BUT NEVER SILENT: a line on accounts receivable, accounts payable, inventory, the SST control or opening-balance equity is reconciled to a subledger, so a journal there moves the control with no document behind it and the aging-vs-control tie-out will show the difference. Such a draft is filed and flagged — it can be approved ONLY on its full review page in Taokeh, never by a one-tap email approval or a deck swipe. Say in `notes` why the control line is right. WHAT NOT TO USE THIS FOR: anything that has a real document. A supplier bill is create_bill_draft, a sale is create_invoice_draft, a paid expense is create_expense_draft, a customer payment is create_receipt_draft. Those carry party, SST and stock consequences a raw journal silently skips. Attach the WORKING PAPER you read the adjustment off — the depreciation schedule, the accrual computation, the bank letter. It rides the draft and the approver sees it beside your figures. Small files inline as attachmentBase64; anything real goes through request_attachment_upload then attachmentToken (never both). Set needsReview and add a SHORT reviewer note in `notes` for any doubt. Filed it wrong? Use revise_draft (kind: 'journal') rather than filing a second one.
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  • Run the Central Command agent console (strategy lifecycle + account actions). Purpose: guide/whoami, create/update strategies, backtest, deploy STOPPED, paper execute, and (explicitly) live orders. Behavior: READ + WRITE. Deploy without execute does NOT move money. Live place_order/close_position/cancel_order require confirm_live=true. Default force_paper=true. Auth: X-Api-Key (linked Connect keys preferred — free). Do not spoof X-Linked-User-Id. Cost: linked Connect keys free; otherwise prepaid / x402 per catalog price for agent-strategy. Rate limit: plan default. Returns: JSON envelope { ok, endpoint, status, data: { ok, guide|whoami|created|deployed|... } }. Guidelines: Start with action=guide then whoami. Prefer paper. Never invent outbound-proxy failures — report real HTTP status/body.
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  • The catalog behind the other tools: indexed subjects/topics (for search_sambodh_knowledge and quiz subject filters), available PYQ years, published current-affairs month slugs, and NewsPulse GS-paper values. Call this first when unsure which parameter values are valid.
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  • START HERE. Interactive guide for an agent that just discovered $BOBAI: what you can ask, what you can do, and which tool to call for each — plus the must-know fee-on-transfer rule.
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  • Get full details for a specific quantum computing paper by its arXiv ID (e.g., "2401.12345"). Use after searchPapers or getLatestPapers when the user wants to dive deep into a specific paper. Returns: complete abstract, all authors, publication date, AI-generated tags with reasons, hook (one-line summary), methodology, gist, and key findings. Requires a valid paper_id from search results. Returns error if not found.
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