get_dataset_statsDiscover what data is available before querying: date ranges, contract counts, symbol counts and sector coverage for the unusual-activity dataset and its daily aggregates. Call this first when unsure about available history.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavioral traits. It implies a read-only, metadata-oriented operation through 'Discover what data is available' and 'Call this first,' which strongly suggests a non-destructive query. It also explains what information is returned, adding behavioral context beyond the bare tool name. It does not explicitly state 'no side effects,' but for a stats/discovery tool this is reasonably transparent.
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: the first front-loads the core purpose and output types, the second gives usage guidance. Every word contributes, with no redundancy or filler. It is concise yet informative.
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 has no parameters, no annotations, and no output schema, the description does well to specify both the output content (date ranges, contract counts, symbol counts, sector coverage) and the dataset scope ('unusual-activity dataset and its daily aggregates'). It also provides usage context. It is slightly incomplete in not specifying whether the stats are global or per-symbol/time-window, but for a discovery tool this is adequate.
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, and the rubric gives a baseline of 4 for 0 params. The description adds no parameter-specific semantics, but none are needed. It does mention the content of the returned data, which is not about parameters. The baseline is appropriate.
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 purpose: 'Discover what data is available before querying' and enumerates specific outputs (date ranges, contract counts, symbol counts, sector coverage). It distinguishes itself from the sibling data-query tools like get_unusual_activity by focusing on dataset metadata rather than actual activity rows.
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 explicitly instructs 'Call this first when unsure about available history,' which gives a clear when-to-use. It also implies using it before other querying tools ('before querying'). However, it does not explicitly mention alternative tools or give when-not-to-use conditions, so it falls short of a 5.
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.