tw-market-data

Ask in natural language

ask

Read-onlyIdempotent

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 beginner's wording into a sourced explanation instead.

★ EVERY SENTENCE CARRIES A CITATION OR IS NOT RETURNED. Vocabulary sentences cite
`glossary:<id>`. Figures cite the dataset row they came from. A sentence whose number cannot be
traced to a retrieved row is DROPPED — it appears in `dropped[]` with status `unverifiable`, and
never in `claims[]`. There is no path by which this tool composes a number from its own memory.

★ TERMS OUTSIDE THE GLOSSARY RETURN `unsupported_term`, NOT A GUESS. The corpus is 99 curated
Taiwan-market terms. CAPM, options greeks and general finance vocabulary are not in it, and the
honest answer is that we do not cover them.

⚠️ Explanations only. It states what a term means and what a figure was; it does not tell you
what to do about either. Args: question, optional as_of (YYYY-MM-DD).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`question`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

The description goes well beyond the readOnly/openWorld annotations by disclosing the citation guarantee, sentence-dropping behavior with dropped[] and status 'unverifiable', the unsupported_term response, and the no-fabrication rule. It also clarifies that this tool only explains and does not give advice, which is critical behavioral context.

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

Conciseness5/5

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

The description is long but densely informative, and every section earns its place: audience, sibling differentiation, citation guarantee, error behavior, and scope limitation. The main purpose is front-loaded in the first sentence, and bold headers make the structure skimmable.

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?

Even with an output schema present, the description explains the semantic meaning of claims[] and dropped[], the unsupported_term case, and the no-advice boundary. This is complete enough for an agent to call the tool correctly in the intended beginner-routing context.

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

Parameters4/5

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

The schema provides only bare titles ('Question', 'As Of') and no descriptions, so the description carries the burden. It adds that question is a plain-language question with examples, and that as_of is optional and formatted YYYY-MM-DD; however, it does not explain the semantic effect of as_of (e.g., historical date context), which is a minor gap.

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?

First sentence states a specific verb ('Answer'), a resource ('plain-language question in Taiwanese-market vocabulary'), and a distinctive output trait ('sentence by sourced sentence'). It then explicitly differentiates from describe_dataset, search_filings, and query_dataset by naming which user type each suits, so an agent can select among siblings.

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

Usage Guidelines5/5

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

The description explicitly says this is for beginners who do not know which dataset they want, and contrasts it with describe_dataset (table already named), search_filings (company disclosures), and query_dataset (returns rows). It also states honest unsupported-term behavior, leaving no ambiguity about when to call or not call this tool.

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.

Resources

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