tw-market-data

Search filings and announcements

search_filings

Read-onlyIdempotent

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 `published_at <= as_of` in SQL
BEFORE ranking, so a backtest cannot retrieve a filing that did not exist yet. `meta.as_of_applied`
echoes the cutoff that actually ran — check it. **Without `as_of` the results include the most
recent filings and are look-ahead UNSAFE for backtesting**; the response says so in `meta.warnings`.

★ REFERENCE CONTEXT, NOT AUTHORITY. Every hit carries `source`, `source_tier`
("official" = MOPS/exchange, "third_party" = press) and a `url`. Read the chunk and judge it
yourself; the ranking is similarity, not importance, and similarity is not evidence.
**Nothing here is investment advice** (`not_investment_advice: true`).

★ A ticker you asked about that returns nothing appears in `coverage.missing` with a reason.
That means NOTHING IS INDEXED for it under those filters — it does NOT mean the company
disclosed nothing. Do not fill the gap.

Args:
    query: what to look for, in Chinese or English (e.g. '匯率風險', 'CoWoS capacity').
    tickers: restrict to these codes, e.g. ['2330','2317'].
    doc_type: e.g. 'mops_major_event', 'financial_note', 'company_news'.
    as_of: knowledge-time cutoff 'YYYY-MM-DD' — use it for anything backtest-shaped.
    source_tier: 'official' to exclude third-party press.
    limit: <= 100.
Returns: {data:[{ticker,doc_type,published_at,source,source_tier,url,chunk_text,similarity}],
          meta:{as_of_applied,point_in_time_safe,embedding_model,warnings,query_id},
          provenance:{...}, coverage:{requested,returned,missing,reason}, freshness:{...}}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`limit`	No
`query`	Yes
`tickers`	No
`doc_type`	No
`source_tier`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/openWorld/idempotent, and the description adds critical behavior beyond those: SQL-level point-in-time filtering before ranking, `meta.as_of_applied` reflecting the actual cutoff, look-ahead unsafety without `as_of`, coverage.missing semantics, and the explicit caveat that ranking is similarity, not evidence. This is model-helpful transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with bolded callouts, an Args section, and a Returns section. Every section adds meaning, though some points (e.g., look-ahead warning, not investment advice) are repeated across prose and meta-field notes, making it slightly heavier than strictly necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a semantic search tool with an output schema, the description is complete: it covers result fields, meta warnings, provenance, coverage missing semantics, and common pitfalls like empty results meaning 'not indexed' rather than 'no disclosure.' An agent has enough context to call it correctly and interpret results properly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries full parameter documentation burden. It does so exceptionally: each parameter is listed with purpose, examples, and constraints (`limit <= 100`, `as_of` format, `tickers` as Chinese/English codes, `doc_type` examples). This goes well beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Semantic search over MOPS filings, financial-statement notes and company news.' It immediately distinguishes itself from keyword filtering with concrete example questions, so an agent can tell it apart from sibling search-like tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear context: use `as_of` for backtest-shaped queries, and use `source_tier: 'official'` to exclude press. However, it does not name sibling alternatives or state explicit when-not-to-use conditions, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0

Disambiguation4/5

The tools are largely distinct: querying, searching, backtesting, risk reads, alerts, memory, and audit functions each have clear homes. A few adjacent pairs (risk_read vs risk_assess, company_health_check vs positioning_read) could be confused, but the descriptions draw explicit boundaries.

Naming Consistency3/5

There are strong consistent clusters like list_*, get_*, run_*, and memory_*, but the *_read suffix alternates with noun-first names like company_health_check, and bare-verb tools like ask, calendar, chart, and screen break the pattern. The naming is readable but not uniform.

Tool Count2/5

Forty tools is well past the 25+ threshold and makes the surface heavy for an agent to navigate, even though the breadth reflects a genuinely wide platform. Several clusters could plausibly be consolidated without losing capability.

Completeness4/5

The tool surface covers the main lifecycle well: discovery, point-in-time querying, filings search and full text, backtesting, research, risk assessment, alerts, memory, approvals, and provenance verification. Minor gaps exist—no strategy management tools, no memory deletion, no bulk export—but agents can work around them.

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