Tw Market Data MCP

List available Taiwan-market datasets (discovery entry point). Returns id / 中文名 / category / tier / one-line description for each. Use this first to find the right data. Args: category: optional, e.g. 'chip'(籌碼) 'fundamental'(基本面) 'price'(行情) 'macro'(總經) 'relation'(關聯/產業鏈) 'derivatives'(期權) 'event'(事件) 'rag_text'(文本). tier: optional minimum plan: 'free' '

FULL semantics of one dataset: grain (what a row is), field meanings+units, ★TIME-CORRECTNESS rules (knowledge_time_field / point_in_time_safe — read before backtesting), relations for cross-table reasoning, agent_hints (when to use), quant_use (which factors). Args: dataset_id.

Query rows with built-in look-ahead protection. ★ POINT-IN-TIME: pass as_of (YYYY-MM-DD) for backtesting/agent-learning. For non-point-in-time-safe datasets (fundamentals, monthly_revenue, dividend_policy…) rows are filtered by DISCLOSURE date <= as_of, so the agent only sees what was public at that moment. Omit as_of only for present-day lookups (warned). ★ IF A VALUE IS IN `c

Traverse the knowledge graph for cross-table / supply-chain reasoning. - dataset_id: returns join-able datasets (+why) to plan multi-table analysis. - ticker: returns its industry value-chain node + peers in the same node (supply-chain reasoning). Args: dataset_id (e.g. 'equity_daily_prices') and/or ticker (e.g. '2330').

Semantic search over MOPS filings, financial-statement notes and company news. Answers questions a keyword filter cannot: "what risks did this company disclose this quarter?", "which companies mentioned CoWoS capacity expansion?" — matching on MEANING, so a paragraph that never uses your exact words still ranks. ★ POINT-IN-TIME: pass as_of (YYYY-MM-DD). Chunks are filtered `pub

Where did this number come from, and when could anyone have known it? (WP-6) ★ THIS IS THE GROUNDING TOOL. Every other tool's answer is supposed to be expandable through this one: given a ticker, a field and a date, it names the dataset that serves that field, the knowledge column, and WHICH as_of rule applies on that date. Use it whenever you are about to state a number as fact.

Prove a row was in the snapshot TWMD published — and check it yourself. Returns the Merkle sibling path, the signed root, and the checkpoint it belongs to. It returns the PATH rather than a yes/no on purpose: a service that answers "yes, it is included, trust me" is the opposite of verifiable. Recompute the root from the leaf and the path; the verifier is ~30 lines and is written

Produce a bibliographic citation for TWMD data — APA, BibTeX, and a re-verifiable token. ★ FOR PAPERS, REGULATORY FILINGS AND ANYTHING A REVIEWER WILL RE-CHECK LATER. A dataset is corrected, backfilled and re-run. Three years from now a reviewer opening our API sees different numbers than the paper, and nobody — author, reviewer, or us — can tell whether the data changed or the au

Read the FULL TEXT of filings and announcements — with proof links and a knowledge cutoff. ★ NOT search_filings. That one ranks passages by similarity and hands you fragments; this hands you whole documents so you can read what was actually said and where it sat in the filing. Similarity is not importance, and a fragment cannot show you its own context. ★ POINT-IN-TIME: pass `a

Answer a plain-language question in Taiwanese-market vocabulary, sentence by sourced sentence. ★ FOR BEGINNERS WHO DO NOT KNOW WHICH DATASET THEY WANT. Ask "PBR 是什麼" or "什麼叫漲跌停" in ordinary words; routing happens on our side. describe_dataset explains a table you already named, search_filings digs through company disclosures, and query_dataset returns rows — this one turns a

Turn rows you already fetched into a Vega-Lite drawing your chat window can render. ★ IT DRAWS; IT DOES NOT FETCH. Hand it the output of query_dataset — this tool never touches the database, so it cannot bypass the as_of filter those rows were selected under. A plotting tool that fetched its own numbers would be a second data path, and a second path eventually disagrees with t

Post-process rows already returned by query_dataset; without rows it returns a clear hint. Turn a spoken shortlist description into explicit numeric cut-offs, and show the cut-offs. ★ THE THRESHOLDS COME BACK WITH THE SHORTLIST. "低本益比" becomes per < 15, and that 15 is printed in applied[] so you can disagree with it. A filter that hands over thirty names without saying where

The Taiwan business-cycle light (NDC monitoring indicator) as a monthly series. ★ THIS IS A REVISED FIGURE, NOT A POINT-IN-TIME ONE. Every response carries revision_basis: as_revised. Our source holds exactly one row per month — the CURRENT value, not the value as first published — and it records no publication date. ★ as_of IS REFUSED, AND THE REFUSAL IS THE POINT. There is

Sort corporate dates into what is still ahead and what has already passed. ★ TWO DATES, NOT ONE. What is "upcoming" is decided by the date the event HAPPENS; as_of filters on the date it was ANNOUNCED. An ex-dividend declared on 2026-08-01 for 2026-09-15 is both already known and still ahead on 2026-08-10. Collapsing the two fields either hides every future date or reports last

Lay two to five named companies side by side on the same measures, gaps marked as gaps. ★ AN ABSENT FIGURE STAYS ABSENT, AND THE COMPANY STAYS ON THE TABLE. A blank cell is reported as available: false, never filled with a zero, a previous period, or by quietly dropping the column. Dropping is the worst of the three: it converts "we do not hold this measure for that company" int

Emit a copy-pasteable HTTP snippet wired to the real endpoint, header and parameter names. ★ FOR WRITING YOUR OWN CLIENT, NOT FOR GETTING DATA. Every other tool here answers a question; this one hands you source code so your program can ask it directly over HTTPS. Nothing is fetched and no rows come back. ★ THE CONSTANTS ARE READ OUT OF THE SERVER, NOT REMEMBERED. Base URL, the `

Replay a saved multi-step routine over rows you fetched, with every step listed. ★ THREE ROUTINES: momentum_scan (rank by a return column), earnings_surprise (actual versus estimate), dividend_capture (which ex-dates are still ahead). ★ A SAVED ROUTINE IS NOT A TRADING VIEW. The names are conventional labels for well-known sequences; what runs is arithmetic over rows you su

Hand an unregistered caller a short taste of an open dataset, plus where to unlock the rest. ★ WHAT AN ACCOUNTLESS CALLER GETS INSTEAD OF A BARE REFUSAL. Somebody arriving through a chat connector with no plan would otherwise meet a flat rejection, which the host model relays as "this service turned you down". ★ WHAT IS ACTUALLY FREE, STATED CONCRETELY. The reference resources re

Submit a point-in-time backtest. NON-BLOCKING: returns a run_id at once; poll get_backtest. job_status tells you where it is: cached (this exact backtest was already computed — the stored result is returned immediately), done (it finished within wait_seconds), or queued / running (call get_backtest(run_id) until it is done or failed). The same spe

Retrieve a previous backtest by run_id — the full record, including why it was rejected. Only runs in YOUR namespace are visible; a run_id belonging to someone else is simply not found. Args: run_id (the twmd_bt_… handle returned by run_backtest).

Re-run a stored backtest and report whether it still produces the same numbers. Same spec, same as_of, same data questions. If the numbers moved, either the engine version changed or the underlying data was restated — both are reported, neither is smoothed over. Args: run_id.

Browse an INDEX of your past backtest runs — ids and headline metrics only, no re-execution. Use when you want to find a run whose id you have forgotten. It never re-computes anything: run_backtest executes a new one, get_backtest opens a single record in full, and replay_backtest re-derives one to check reproducibility. This is the catalogue, not any of thos

Remember something, with its sources and its knowledge time. Nothing is ever overwritten: saving a factor_def or watchlist under an existing key SUPERSEDES the previous version (both rows survive, so "what did I believe in June?" stays answerable), and saving identical content twice is a no-op rather than a duplicate. Args: kind: 'query' | 'factor_

Recall your own memories — hybrid (semantic + exact-term), with provenance attached. Every result carries where it came from (source_query_ids, replayable), when it was believed (valid_from/valid_to) and what knowledge time it is about (as_of), plus a recall block stating which model and which filters produced the answer. Args: query: what yo

Read the tickers on one named watchlist, as it stands right now. A curated roster you maintain — distinct from memory_search, which digs through everything you ever recorded, and from list_alerts, which is about price triggers rather than symbols you are following. Args: key — the roster's name, default 'default'.

Re-run a remembered query by its twmd_q_… id, through the read API's own replay store. This is what makes a recalled finding checkable: the memory says where it came from, and this fetches that same data again. It never re-executes the query by another route — two implementations of "replay" would be two answers to a question whose whole value is having one.

Run a multi-agent research pass and return a structured, sourced report. Six roles run in order — data analyst, factor researcher, backtest engineer, risk officer, portfolio manager, compliance officer. Each step's output carries the query_ids behind it; risks are reported alongside results, not beneath them; and anything the run could not do is listed as a limit

Retrieve one of YOUR previous research reports. Others' runs are simply not found. Args: research_id — the identifier run_research returned when it started that run.

Financial actions proposed by your research runs that are waiting for a human decision. Nothing here has been executed or ever will be by this system. These are proposals.

No parameter schema on file.

Record a HUMAN's approval of a proposed action. This writes an audit record naming who approved what, and when. It does NOT execute the action: TWMD has no order or funds path, by design. Execution, if any, happens elsewhere and is performed by a person. Args: action_id: from list_pending_actions or a report's proposed_actions. approver

Show the price-trigger rules you have armed, and whether each is still armed. A read-only inventory of thresholds you asked to be watched — it arms nothing and cancels nothing (set_price_alert arms, delete_alert cancels). Another customer's triggers are simply not visible here.

No parameter schema on file.

Leave a standing instruction: tell me when this symbol crosses this price. The alert OUTLIVES this conversation. It is evaluated against official daily closes by a resident agent and delivered to your realtime stream and to any webhook endpoints you have registered — signed, retried, and de-duplicated so one crossing is one notification. This is a NOTIFICATION, no

Cancel one armed price trigger permanently, by its rule id. Disarms a single watch so it will not fire again — the opposite of set_price_alert, and unlike list_alerts it changes state rather than reporting it. Cancellation is irreversible: re-arming means creating a fresh trigger. Naming somebody else's rule id cancels nothing at all. Args: rule_id.

Measure a portfolio you state against limits you state, on official point-in-time prices. Reports concentration and peak-to-trough drawdown, and NAMES every position it could not price rather than quietly assessing the rest — an assessment covering 60% of a portfolio without saying so is worse than none. Any breach produces a PROPOSAL (e.g. "reduce 2330") that req

What YOUR agents have actually done, from the durable audit trail. Every resident agent records what it did and ON WHAT BASIS — the rule and the two closes behind an alert, the coverage and limits behind a risk finding, the run_id and declared trial count behind a factor verdict. This is that trail, and it survives deploys. Reports which agents have recorded NOTHI

Verdicts from the overnight factor search on YOUR namespace — including the rejections. The rejections are returned deliberately. A research log that keeps only the winners is the highlight reel overfitting lives in, and the acceptance RATE is the number that tells you whether the anti-overfitting gate is doing its job: a search that accepts most of what it tries h

一檔股票的誠實體檢:成長、獲利品質、估值、籌碼、紅旗,每個數字可驗證。 ★ 這是事實整理,不是投資建議、不是預測、不是目標價。輸出帶 does_not_prove,轉述時請一併保留。 ★ POINT-IN-TIME:as_of 逐資料集尊重揭露時差 —— 月營收用法定截止日推得的 估計時點,財報用出表日,估值是當日。不給 as_of 就用「現在可知的最新」, 不是今天。 ★ 沒看到的東西會列在 cannot_see,並說明是「查了沒有」「查詢失敗」還是 「散佈權未取得」—— 三者不同。⚠️ 不要替 cannot_see 裡的項目估算或補值。 ★ checked_and_clear 是「查過而且確認沒有」(例如近一年無

籌碼結構:法人分項、融資融券、借券使用率、大戶集中度、董監質押。 ★ 事實整理,不是投資建議、不是預測、不是目標價。輸出帶 does_not_prove。 ★ POINT-IN-TIME:as_of 逐資料集尊重揭露時差;不給就用「現在可知的最新」。 ★ 看不到的在 cannot_see,並分「查了沒有 / 查詢失敗 / 散佈權未取得」。 ⚠️ 逐券商分點進出永遠在 cannot_see —— 散佈權未取得,不要替它估算。 ★ PRECONDITIONS:自己取數,rows 不需要先用 query_dataset 備好;requires 一個 已識別的 session。方案沒涵蓋的資料集以 not_licensed 進 cannot_see,不整支失敗。

總經 Regime:景氣信號、利率與殖利率曲線、匯率、資金流。 ★ 這支講的是整個市場,不是任何一檔個股 —— 所以它不收 ticker。 ★ 事實整理,不是投資建議、不是預測。輸出帶 does_not_prove。 ★ ⚠️ macro_worldbank 類的來源會回溯修訂歷史年度:今天讀到的舊年度數字 和當時看到的不是同一個,pit_notes 會標出來。 ★ Args:只有 as_of(選填)—— 它不收 ticker,因為主體是整個市場。 ★ Returns:同一個誠實信封;subject 固定是 "TW_MARKET"。 ★ 例如問「現在景氣循環在哪個位置」就是這一支,而不是問某一檔。 ★ PRECON

供應鏈與同業:產業鏈位置、同業分組、轉投資、外銷訂單 vs 營收。 ★ 事實整理,不是投資建議、不是預測。輸出帶 does_not_prove。 ★ ⚠️ company_peer_groups 無日期欄:指定過去的 as_of 時,回的是現況分組, pit_notes 會標「非當時分類」。 ★ ⚠️ 質化的客戶/供應商關係與分部營收在年報附註,那條來源尚未建置 —— 固定列在 cannot_see(status=no_loader),不是這次查失敗。 ★ Args:ticker(例如 "2330")、as_of(選填)。 ★ Returns:誠實信封;外銷訂單與營收的背離會 listed in concerns。

事件與市場結構風險:隱含波動率、注意處置、裁罰訴訟、放空限制、因子。 ★ ⚠️ 部位集中度與回撤不在這裡 —— 那是 risk_assess 的職責。重算一份會產生 第二個答案,而兩個都帶著我們的名字。 ★ checked_and_clear 是「查過而且確認沒有」(例如近一年無裁罰),和 cannot_see 的「不知道」是兩回事。 ★ 事實整理,不是投資建議、不是預測。輸出帶 does_not_prove。 ★ Args:ticker(例如 "2330")、as_of(選填)。 ★ Returns:誠實信封;事件風險 listed in concerns,查過確認沒有的 listed in `checked_a