"SST" matching MCP tools:

• resolve_entityA

  Ssb NoA

  "What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns `figi_candidates` to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

  ConnectorNo auth

• polymarket_arbitrageA

  Ssb NoA

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

  ConnectorNo auth

• polymarket_fill_riskA

  Ssb NoA

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

  ConnectorNo auth

• create_quote_draftA

  Taokeh MCP serverA

  File a quotation DRAFT into Taokeh from a request you've read or been told. A quote is an ESTIMATE — it does NOT post to the books or move stock; it creates a pending draft the user reviews and approves in Taokeh, which posts a real quotation they can then convert to an invoice or delivery order. Shape the fields with resolve_customer + resolve_product first. The server re-computes every line quantity from the tally and the grand total (with SST) — so present your working, but the server's figures are authoritative. A quote may have no customer (a walk-in estimate). Set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what to double-check — not lengthy reasoning) for any doubt. If you read the request off a document, attach the original document you extracted from — the owner sees it beside the draft at review (s.82 record-keeping) and it lands on the posted quote automatically on approval. Small files: attachmentBase64 + attachmentMediaType inline. Send attachmentBytes (the original file’s decoded size) with it so a truncated base64 is rejected instead of filed. Anything bigger: request_attachment_upload → PUT the bytes → pass the returned attachmentToken — unless your shell cannot reach taokeh.my (a sandboxed client behind a network allowlist), in which case inline it anyway, with attachmentBytes; never both.

  ConnectorOAuth

• create_purchase_order_draftA

  Taokeh MCP serverA

  File a PURCHASE-ORDER draft into Taokeh — an order you intend to PLACE with a supplier, typically read off their quotation. It POSTS NOTHING and MOVES NOTHING: a purchase order is an intent to buy, so approving it creates no journal entry, no stock movement and no amount owing. The books move later, when the goods arrive and the user converts the order into a bill on its page in Taokeh — a step this tool never takes for them. Approval also saves the order as a DRAFT; issuing it to the supplier is the user's own tap, and Taokeh emails nobody. INVENTORY-ONLY — every line must be a product the company stocks; a service or one-off non-stock charge belongs on the manual purchase-order form. NO SST on this document: a purchase order in Taokeh carries no tax code and no tax line, and input SST is settled on the bill against the supplier's tax invoice. Shape the fields with intake_contract(doc_type:'purchase_order') + resolve_vendor + resolve_product first. The server re-computes every line quantity from the tally and the order total — so present your working, but the server's figures are authoritative. Set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what to double-check — not lengthy reasoning) for any doubt. Filed it wrong? Use revise_draft (kind: 'purchase_order') rather than filing a second one. Goods that have ALREADY arrived are not this door — use create_bill_draft.

  ConnectorOAuth

• search_withinA

  Ssb NoA

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

  ConnectorNo auth

• rememberA

  Ssb NoA

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

  ConnectorNo auth

• nausika_marine_forecastA

  NausikaA

  Marine + atmospheric forecast at (latitude, longitude in decimal degrees). Returns waves, swell, currents, SST, wind, gusts, pressure, weather_code, visibility, precipitation, temperature, UV. Directions FROM. Presets: now, today, tactical (default 3d), planning (8d), extended (14d), none. Windows: start_hour/end_hour, start_date/end_date, forecast_hours, forecast_days. Extra via variables[]. Covers only a rolling window near today (~16 days ahead for atmospheric data, ~8 days for marine/wave data) — for dates further out, use nausika_seasonal_forecast (1-7 month outlook) instead. Pass include_percentiles=true to also fetch the ECMWF IFS-ENS 50-member ensemble and expose <var>_p10/_p50/_p90 series for atmospheric variables (precipitation, wind, gusts, pressure, temperature, etc.) alongside the deterministic forecast. Use it when forecast uncertainty matters — convective setups, planning/extended horizons, or any time the deterministic precip total looks inconsistent with the precipitation_probability. Waves/swell/currents/SST remain deterministic (no public ensemble). Default false to keep payload small. Example: latitude=41.39, longitude=9.16, preset="today". Chain nausika_geocode for place→coords.

  ConnectorNo auth

• search_withinA

  Dst DkA

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

  ConnectorNo auth

• entity_profileA

  Dst DkA

  "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit `notes` line, not a bare failure. `type` accepts "company" or "ticker" interchangeably — both take the same `value` shapes above.

  ConnectorNo auth

• compare_entitiesA

  Jpl SsdA

  "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

  ConnectorNo auth

• recent_changesA

  Jpl SsdA

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

  ConnectorNo auth

• validate_claimA

  Jpl SsdA

  "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

  ConnectorNo auth

• polymarket_arbitrageA

  Jpl SsdA

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

  ConnectorNo auth

• rememberA

  Jpl SsdA

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

  ConnectorNo auth

• extract_panA

  GST ValidatorA

  Extract the 10-character PAN embedded in a GSTIN (positions 3-12, 1-indexed). Throws if the GSTIN is the wrong length or the embedded PAN is malformed. Does NOT verify the check character — use validate_gstin for that.

  ConnectorNo auth

• set-envA

  mcpA

  Set an environment variable for a project. Variables are encrypted at rest (AES-256-GCM) and injected at container runtime. NOTE: DATABASE_URL, PGHOST, PGPORT, PGUSER, PGPASSWORD, and PGDATABASE are all auto-injected for the managed PostgreSQL database — you do NOT need to set any of them manually. The PORT variable is auto-managed: 8080 for auto-detected frameworks (Next.js, Node.js, Python), or auto-detected from the Dockerfile EXPOSE directive for custom Dockerfile builds. IMPORTANT: Changing env vars does NOT auto-redeploy. You must call deploy or use the redeploy API endpoint to apply changes. For Next.js apps, NEXT_PUBLIC_* variables must be set BEFORE deploying since they are embedded at build time.

  Connector

  Destructive

  No auth

• create_credit_note_draftA

  Taokeh MCP serverA

  File a CREDIT NOTE draft into Taokeh — a sales return, short delivery, price correction or allowance that reduces what a customer owes. This does NOT post to the books: it creates a pending draft the user reviews and approves in Taokeh, and only then does it reduce the customer's balance, reverse the revenue and SST, and (if the user says the goods came back) put stock back. Shape the fields with intake_contract(doc_type:'credit_note') first. RESOLVE THE ORIGINAL INVOICE FIRST: a credit note is issued AGAINST an invoice — find it with search_documents and pass its id as originalDocId with kind:'against_invoice'. Only a credit with NO source invoice is kind:'allowance', and an allowance is never linked to an invoice, never capped by one, inherits no SST codes and never restocks — so do not file a real return as one; ask the user which invoice it is against. Send all amounts POSITIVE (Taokeh stores the credit note negative itself). A line can only credit back as MANY units as that invoice actually sold, net of earlier credit notes — an over-quantity line is refused naming what is left, so read the invoice's own unit of measure rather than converting it (1 carton is not 100 pieces). Whether goods physically came back into stock is the USER's decision at approval — `goodsReturned` is only a hint that pre-ticks their checkbox on the full review page, and a one-tap approval always posts money-only. Set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what to double-check) for any doubt. Filed it wrong? Use revise_draft (kind: 'credit_note') rather than filing a second one. IS A CREDIT NOTE EVEN THE RIGHT DOCUMENT? Only when something CHANGED AFTER the sale — goods came back, a price was renegotiated, a discount was agreed later. If the sale simply never happened (keyed twice, wrong company, cancelled before delivery), the owner VOIDS the invoice instead: Sales → the invoice → Void, in Taokeh. There is deliberately no AI lane for voiding. And if the sale did happen but the invoice was typed wrong — wrong line, wrong quantity or price, wrong date — that is a correction, not a credit: use update_invoice_draft.

  ConnectorOAuth

• create_purchase_order_draftA

  mcpA

  File a PURCHASE-ORDER draft into Taokeh — an order you intend to PLACE with a supplier, typically read off their quotation. It POSTS NOTHING and MOVES NOTHING: a purchase order is an intent to buy, so approving it creates no journal entry, no stock movement and no amount owing. The books move later, when the goods arrive and the user converts the order into a bill on its page in Taokeh — a step this tool never takes for them. Approval also saves the order as a DRAFT; issuing it to the supplier is the user's own tap, and Taokeh emails nobody. INVENTORY-ONLY — every line must be a product the company stocks; a service or one-off non-stock charge belongs on the manual purchase-order form. NO SST on this document: a purchase order in Taokeh carries no tax code and no tax line, and input SST is settled on the bill against the supplier's tax invoice. Shape the fields with intake_contract(doc_type:'purchase_order') + resolve_vendor + resolve_product first. The server re-computes every line quantity from the tally and the order total — so present your working, but the server's figures are authoritative. Set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what to double-check — not lengthy reasoning) for any doubt. Filed it wrong? Use revise_draft (kind: 'purchase_order') rather than filing a second one. Goods that have ALREADY arrived are not this door — use create_bill_draft.

  ConnectorOAuth

• create_credit_note_draftA

  mcpA

  File a CREDIT NOTE draft into Taokeh — a sales return, short delivery, price correction or allowance that reduces what a customer owes. This does NOT post to the books: it creates a pending draft the user reviews and approves in Taokeh, and only then does it reduce the customer's balance, reverse the revenue and SST, and (if the user says the goods came back) put stock back. Shape the fields with intake_contract(doc_type:'credit_note') first. RESOLVE THE ORIGINAL INVOICE FIRST: a credit note is issued AGAINST an invoice — find it with search_documents and pass its id as originalDocId with kind:'against_invoice'. Only a credit with NO source invoice is kind:'allowance', and an allowance is never linked to an invoice, never capped by one, inherits no SST codes and never restocks — so do not file a real return as one; ask the user which invoice it is against. Send all amounts POSITIVE (Taokeh stores the credit note negative itself). A line can only credit back as MANY units as that invoice actually sold, net of earlier credit notes — an over-quantity line is refused naming what is left, so read the invoice's own unit of measure rather than converting it (1 carton is not 100 pieces). Whether goods physically came back into stock is the USER's decision at approval — `goodsReturned` is only a hint that pre-ticks their checkbox on the full review page, and a one-tap approval always posts money-only. Set needsReview and add a SHORT reviewer note in `notes` (one or two sentences naming what to double-check) for any doubt. Filed it wrong? Use revise_draft (kind: 'credit_note') rather than filing a second one. IS A CREDIT NOTE EVEN THE RIGHT DOCUMENT? Only when something CHANGED AFTER the sale — goods came back, a price was renegotiated, a discount was agreed later. If the sale simply never happened (keyed twice, wrong company, cancelled before delivery), the owner VOIDS the invoice instead: Sales → the invoice → Void, in Taokeh. There is deliberately no AI lane for voiding. And if the sale did happen but the invoice was typed wrong — wrong line, wrong quantity or price, wrong date — that is a correction, not a credit: use update_invoice_draft.

  ConnectorOAuth

• create_journal_draftA

  mcpA

  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 company'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. Prefer request_attachment_upload → attachmentToken; if your shell cannot reach taokeh.my (a sandboxed client behind a network allowlist) send attachmentBase64 + attachmentMediaType inline instead — correct even for a full PDF — always with attachmentBytes, the file’s decoded size on disk, so a truncated paste is rejected instead of filed. 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.

  ConnectorOAuth