Screen Chinese ADRs listed on US exchanges (Alibaba, JD, PDD, NIO, etc.).
Args: criteria: filter dict (sector, 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 and openWorldHint, so the description aligns without contradicting. It adds no further behavioral context beyond the screening action, but this is sufficient given the annotation coverage.
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, front-loaded with purpose, and structured with clear Args section. Every sentence adds value with no wasted words.
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?
For a simple screening tool with 2 parameters and no output schema, the description adequately covers purpose and parameter roles. It could detail return format, but sibling similarity and annotations keep it functional.
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 coverage is 0%, so the description must compensate. It explains criteria as a filter dict with examples (sector, market cap, P/E) and limit as max rows, adding meaningful guidance beyond the bare schema.
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 Chinese ADRs listed on US exchanges, with specific examples (Alibaba, JD, PDD, NIO), distinguishing it from sibling tools for 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 context is clear: use for Chinese ADRs on US exchanges. However, no explicit when-not-to-use or alternative tool guidance is provided, though sibling names imply the domain.
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.