Get normalized price predictions for a stock ticker, including expected short, mid, and long-term values, with a time series of mid, low, and high estimates.
Normalized ticker prediction with a time series: { ticker, name, type, last_update, expected_short/mid/long, series: [{date, mid, low, high}, ...], }
| Name | Required | Description | Default |
|---|---|---|---|
| req | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description only shows output structure, not behavioral traits like data freshness, update frequency, or rate limits. Agent lacks critical operational context.
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 but as a JSON blob it is messy and not front-loaded with a clear action statement. It conveys less than it could in a concise manner.
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?
With no output schema, no annotations, and many sibling tools, the description fails to provide complete context. Agent cannot determine what data is returned or how to interpret results reliably.
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%, and the description does not explain the parameters (ticker, prediction_type, format). It only lists output fields, leaving the meaning and usage of inputs unclear.
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 is a JSON format showing output fields but does not explicitly state that the tool retrieves predictions for a ticker. It is vague and leaves the purpose inferred.
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?
No guidance on when to use this tool versus the many sibling 'by_ticker' tools. There are no instructions about appropriate contexts or alternatives.
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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