get_market_breadthS&P 500 market breadth: the percentage of index members above their 50-day and 200-day moving averages, with RSI, trend and 52-week range for each breadth series. A gauge of how broad the current advance or decline is.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| pct_above_50d_ma | No | ||
| pct_above_200d_ma | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds useful context about the data content (percentage above MAs, RSI, trend, 52-week range) and its interpretation as a breadth gauge, going beyond the safety profile provided by 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 two sentences, front-loaded with the tool's focus and key metrics, and contains no redundant information. Every clause adds value.
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 tool's simplicity (no parameters, output schema present), the description is complete. It explains what the tool measures, what components are included, and the purpose of the data, making it self-sufficient for selection and basic understanding.
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 tool has zero parameters, so the description does not need to explain parameter semantics. The baseline score of 4 is appropriate because there is no parameter complexity to document.
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 that the tool provides S&P 500 market breadth, specifically the percentage of members above 50-day and 200-day moving averages, along with RSI, trend, and 52-week range. This is a specific verb+resource combination that distinguishes it from sibling tools like get_sentiment_data or get_relative_strength.
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 provides clear context on when to use the tool—when assessing the breadth of an advance or decline. It does not explicitly mention alternatives or exclusions, but the purpose is unambiguous enough for an agent to select it over other market analysis tools.
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 maps to a distinct data category or function (prices, indicators, levels, sentiment, macro, crypto, intermarket, breadth, news, etc.). The few related tools are clearly separated by current vs. historical data, specific ratios vs. multi-lens overviews, or news lookup vs. news search.
The majority of tools follow a consistent get_<noun> pattern with snake_case (e.g., get_price_history, get_technical_indicators). Two news tools use a public_ prefix instead, creating a minor but visible inconsistency.
15 tools is within the ideal range for a market-data server and each tool covers a meaningful slice of the domain without redundancy. The count feels well-scoped for the server's purpose.
The tool surface is impressively broad, covering prices, indicators, sentiment, macro, crypto, intermarket analysis, news, and methodology. However, common data types like fundamentals (P/E, balance sheets) and options chains are absent, leaving a few potential user questions unanswered.