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Stockfacts

by io.github.AAAZZZR
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Screen Taiwan stocks

tw_screen_stocks
Read-onlyIdempotent

Taiwan companies listed on TWSE or TPEx and still trading (or ETFs with industry=etf), filtered by market and TWSE/TPEx industry code, sorted by market cap (default, largest first) or code, 100 per page. Each row has code, name (Traditional Chinese), market, industry, the latest close and change, the published P/E, and market_cap (the latest close times shares issued, calculated by Livermore) with url. industries lists every industry code with its Chinese name and number of companies, and etf_count the number of ETFs, so a first call without industry shows the codes (24 is semiconductors). An unknown industry code returns an error. Rows have no English names: use tw_get_stock for name_en and the full profile. Taiwan conventions: money is in New Taiwan dollars (currency TWD), share counts are in shares (1 lot = 1,000 shares), prices are in TWD and fields ending in _pct are percentages (12.5 means 12.5%). market is TWSE (listed) or TPEx (OTC). Names are in Traditional Chinese, with name_en where available. url links to tw.livermore.club, whose pages are in Traditional Chinese only. Keep attribution when you present the data: Taiwan's Open Government Data License requires naming its sources, and fields listed in computed_by_livermore are Livermore's calculations, not official figures.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dirNodesc (default) or asc.desc
pageNoPage number; each page has 100 rows. Check has_more.
sortNomarket_cap (default; calculated by Livermore) or code.market_cap
marketNoall (default), twse (listed on the Taiwan Stock Exchange) or tpex (Taipei Exchange).all
industryNoA TWSE/TPEx industry code such as 24 (semiconductors) or 'etf' for ETFs instead of companies; the result's `industries` lists every code. Default: all companies.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
noteYes
pageYes
as_ofYesThe trading day of the closes and market caps.
filtersYes
currencyYesEvery amount of money in this result is in New Taiwan dollars (TWD).
has_moreYes
companiesYes
etf_countYesListed ETFs (industry=etf).
page_sizeYes
industriesYesEvery industry code with its name in Traditional Chinese (TWSE and TPEx names joined when they differ) and the number of companies.
attributionYesRequired attribution: Taiwan's Open Government Data License requires naming the sources when you present or pass on this data, and Livermore's own calculations must be kept apart from the official figures.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds substantial extra context: 100 rows per page, error behavior on unknown codes, the attribution/Open Data License obligation, currency and share-count conventions, and which fields are Livermore-computed rather than official.

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?

It is a long description, but it is front-loaded with the core screening behavior and each sentence carries distinct Taiwan-specific information (units, license, computed fields). Density is justified by the domain conventions, though it could be trimmed slightly.

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?

Given an output schema exists, the description need not explain returns, yet it still covers rows, industries, etf_count, conventions, and legal attribution. Nothing needed to invoke or interpret the tool correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds genuine meaning: industry 24 = semiconductors, 'etf' as a special sentinel, market values mapped to TWSE (listed) vs TPEx (OTC), and the default sort by market cap.

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?

States a specific verb+resource: screening Taiwan companies listed on TWSE or TPEx (plus ETFs), with filter, sort, and pagination scope. It distinguishes itself from tw_get_stock, which it explicitly directs users to for full profiles.

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?

Gives explicit when-to-use guidance: first call without industry shows available codes, unknown code returns an error, and for name_en/full profile use tw_get_stock instead. Alternatives and conditions are named directly.

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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