Screen Taiwan Stock Exchange (TWSE) — semiconductors heavyweights (TSMC, MediaTek, etc.).
Args: criteria: filter dict (industry, market cap, P/E, etc.) limit: max rows to return
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| criteria | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description's burden is lower. The description adds that it screens stocks with criteria and limit, which is consistent. No behavioral details beyond that (e.g., rate limits, pagination), but the safety profile is already clear from annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the main purpose. The parameter explanations are concise. However, the format mixes a one-line summary with a Python docstring style for Args, which is acceptable but could be more streamlined.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the large number of sibling screen tools, this description clearly identifies the target market (TWSE) and provides essential parameter information. It is complete enough for an agent to understand that this tool screens Taiwan stocks with basic filters. No output schema, but the return type is implied.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains 'limit' as 'max rows to return' and 'criteria' as a 'filter dict (industry, market cap, P/E, etc.)'. This adds basic meaning, but the examples are limited and do not specify exact keys or value formats. Adequate but not thorough.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool screens the Taiwan Stock Exchange (TWSE) with a focus on semiconductor heavyweights. It uses a specific verb 'screen' and identifies the resource (TWSE), distinguishing it from sibling tools targeting other markets. However, the mention of 'semiconductors heavyweights' may over-specify the scope, as the criteria parameter allows arbitrary filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for screening Taiwan TWSE stocks, but does not provide explicit guidance on when to use it versus siblings (e.g., other market screeners). It lacks when-not scenarios or alternatives, leaving the agent to infer that the choice is based on market name.
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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Each tool targets a distinct geographic exchange, with no overlap in markets screened. The descriptions specify the exchange and indices, making it easy for an agent to select the correct tool for a given country.
All tools follow the exact same naming pattern: 'screen_<country>_<exchange>' in snake_case, providing a clear and predictable structure. There are no deviations or mixed conventions.
26 tools is on the high side, but appropriate for a global equities screener covering many countries. Each tool serves a distinct market, so the count is justified by the breadth of coverage.
The set covers most major global equity markets, including US, China, India, Japan, and many others. However, some notable markets like Russia or some European exchanges are missing, and there is no tool for screening ETFs or indices directly.