[US Stock Info] Get news for a given ticker symbol.
Args:
ticker: str
The ticker symbol of the stock to get news for, e.g. "AAPL"
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds no extra behavioral context beyond the operation itself (e.g., no mention of rate limits or news source specifics), but it doesn't contradict the annotations. With annotations present, the bar is lower, and the description is adequate.
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: a one-line purpose statement followed by a structured args section. No wasted words, and the parameter is clearly documented. The [US Stock Info] prefix adds light context without bloat.
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 one simple parameter, existing output schema, and comprehensive annotations, the description is complete. It covers the tool's purpose and the parameter semantics, while the output schema handles return values. There is no significant missing information for an agent to correctly select and invoke this tool.
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?
The input schema has zero description coverage for the parameter, but the description provides a full docstring: 'The ticker symbol of the stock to get news for, e.g. "AAPL".' This explains the semantic meaning and gives an example, fully compensating for the schema's lack of detail.
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's function: 'Get news for a given ticker symbol.' This is a specific verb+resource pair that distinguishes it from sibling tools like get_financial_statement or get_historical_stock_prices, which target different data types.
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 clearly implies the tool should be used when you need news for a specific ticker, as evidenced by the parameter requirement. It doesn't explicitly name alternatives or exclusions, but the context is unambiguous; an agent would know to use this for news retrieval versus other stock-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool provides a distinct type of stock data: news, financial statements, historical prices, market overview, recommendations, and stock info. There is no functional overlap between them; the purpose of each is clear and mutually exclusive.
All tool names follow the consistent get_ verb prefix followed by a descriptive resource, such as get_finance_news and get_historical_stock_prices. The naming pattern is uniform and predictable across the entire set.
With six tools, the server is well-scoped for stock and market data retrieval. Each tool covers a major data category without unnecessary duplication, making the count appropriate and manageable.
The tool set covers key stock data needs including news, financial statements, historical prices, market overview, recommendations, and stock summaries. Missing features like direct quote or sector performance are minor and can be worked around with existing tools.