Screen Hong Kong-listed stocks via HKEX (Hang Seng + Mainboard + GEM).
Args: criteria: filter dict (industry, market cap, P/E, dividend yield, 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 mention of parameters adds some context but does not disclose additional behavioral traits beyond what annotations provide.
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 extremely concise with two short sentences, each serving a clear purpose. No unnecessary words; every sentence earns its place.
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?
The description lacks details about the output format or return values. Given no output schema, the description should specify what the tool returns (e.g., list of stocks). It is adequate but incomplete.
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?
With 0% schema description coverage, the description partially compensates by explaining the 'criteria' parameter as a filter dict with examples (industry, market cap, P/E, dividend yield) and 'limit' as max rows. This adds significant meaning beyond the empty schema, though details are still vague.
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 Hong Kong-listed stocks via HKEX, specifying the indices (Hang Seng, Mainboard, GEM). The verb 'Screen' and the resource 'stocks' are specific, and the context distinguishes it from siblings which cover other markets.
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 tool's purpose is clear from the name and context, but the description does not explicitly state when to use this tool over alternatives or provide usage exclusions. However, the sibling tools are differentiated by market, making it obvious.
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.