get_sector_flowWhich GICS sectors the day's unusual options premium is concentrating in: total premium, volume, average net delta and bullish/bearish symbol counts per sector.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | Trading day, YYYY-MM-DD. Defaults to the latest available day. | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It conveys the aggregation level and metrics, but does not state whether all sectors are included or only those with unusual activity, how the 'limit' parameter affects results, or any data caveats. This is a moderate level of transparency.
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 a single well-structured sentence with a colon-delimited list of metrics. Every word contributes information, with no fluff or repetition, making it highly efficient.
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 tool is moderately complex with two parameters and no output schema. The description covers the core return values but lacks usage context, ordering, edge cases, or how this fits into a broader analysis workflow. It is adequate but not complete for an agent without prior knowledge.
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 description does not mention any parameters. The date parameter has a schema description, but 'limit' has none and the description adds no meaning for it, leaving its purpose (e.g., limiting number of sectors) unclear. Schema coverage is only 50%, so the description should compensate but does not.
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 uses a specific question format to identify GICS sectors as the grouping dimension and explicitly lists the output metrics (total premium, volume, average net delta, bull/bear symbol counts). This clearly distinguishes it from siblings like get_symbol_flow or get_expiry_concentration, which operate at different granularities.
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 explicit when-to-use guidance or alternatives are provided. There is no mention of how this tool compares to other sector-level or flow-related tools, leaving the agent to infer its purpose from the output description alone.
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 analytical view (contract scan, per-symbol detail, history, sentiment, etc.), with clear boundaries. The only minor overlap is between get_symbol_flow and get_unusual_activity when filtering by a single symbol, but descriptions clarify the difference (single-symbol detail vs. broad scanner).
All tool names follow a consistent get_<object> pattern (e.g., get_expiry_concentration, get_oi_changes), with ping as the standard exception for liveness checks. The naming is predictable and uniform.
13 tools is well within the ideal range for a specialized data analytics server. Each tool covers a distinct query type and earns its place without redundancy.
The tool surface comprehensively covers the domain of unusual options flow discovery and analysis: data discovery, contract-level scans, per-symbol views, time series, sentiment, and market-wide aggregations. No obvious gaps or dead ends.