Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover readOnly/openWorld/destructive, so the description's added value is real: it discloses coverage window (2014→present), lookahead safety (point-in-time), and what is NOT returned (price columns stripped). It also states the access model — free with X-ORKID-API-Key or $0.01 USDC/call via x402 — which is behaviorally important for call planning.

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?

Front-loaded with the purpose and payload size before parameters and pricing; no filler sentences. The em-dash feature enumeration is dense but each item maps to a plausible agent intent, so nothing is gratuitous.

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, the description carries the return-shape burden and does so: row granularity (per trading day), feature count, feature categories, and quality flags. Combined with the coverage window and access model, an agent has enough to call this correctly.

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 100%, so all four parameters are already documented. The description restates the ticker requirement, date format, and limit default/max, adding almost no meaning beyond the schema.

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?

States a specific verb+resource ('Daily abstract state vectors for a US equity ticker') and quantifies the payload ('37 derived features per trading day') with named feature families. It clearly distinguishes this from sibling timeseries tools such as sv_futures and sv_fsds.

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?

'Price columns stripped — derived features only' implicitly routes the agent away from this tool when raw prices are needed, and 'point-in-time safe, 2014→present' sets a viable context. However, it never names an alternative or states an explicit when-not condition, so the routing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.