df_status

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

No annotations exist, so the description carries the full burden. It helpfully discloses that the call is free (meaningful next to the x402_* siblings, which imply payment), but says nothing about cost/latency, caching, or whether results are stable snapshot values.

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?

Two very short clauses, front-loaded with the cost fact and then the core purpose. Slightly telegraphic ('Free.' as a fragment) but nothing is wasted.

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?

For a simple, argument-free read tool this covers the essentials: it names the datasets covered and what value is returned per dataset. With no output schema, it could specify the response shape (timestamps, keys) a bit more, but an agent has enough to call it 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?

The tool takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate and it correctly presents a no-argument status call.

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 concretely what it returns: the last-refresh time for each of four named datasets (insider trades, prediction markets, macro, x402 economy). That is a specific resource with scope, distinguishable from sibling query tools like insider_latest or macro_latest, though it never explicitly contrasts itself with df_catalog, which may sound adjacent.

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?

Usage is implied – check freshness before relying on a dataset – and 'Free' signals there is no x402 payment cost. However, there is no explicit when-to-use versus df_catalog or any statement of what to do when data is stale.

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