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

Beyond the readOnly/non-destructive annotations, it discloses cost ('free'), dataset scope (~380 US consumer-goods makers), the meaning of tier (1 largest, 3 smallest, weekly only), and the exact fields returned (ISIN, ticker, name, tier, first/last day of data). That tier caveat matters behaviorally: tier 3 has coarser weekly coverage, which an agent would otherwise miss.

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?

Three compact sentences, front-loaded with the data domain and scale, then the negative guidance, then the return fields. No filler or restatement of the title.

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 3-param read tool with no output schema, the description covers purpose, scope, cost, tier semantics and the return fields — everything needed to call it correctly. Remaining details (limit bounds, empty-query default) are already handled by the schema's defaults and the query description.

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?

With only 33% schema description coverage, the description compensates well: 'by name or ticker' clarifies query semantics, and the tier gloss ('1 largest, 3 smallest, weekly only') gives the bare enum real meaning. The limit parameter is left entirely to the schema (default 25, max 400), so coverage is good but not complete.

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: looking up companies for which DecaData has point-of-sale data, with concrete scope (~380 US consumer-goods makers, examples PepsiCo/Kraft Heinz) and lookup keys (name or ticker). It also explicitly excludes what the tool is not (products, prices, deals), making it unmistakable against siblings like quote_sales or get_yoy_growth.

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?

Gives clear when-to-use framing ('For company sales research, not shopping') plus explicit exclusions for products, prices and deals. It doesn't name sibling tools as alternatives, so routing relies on the reader inferring that this is the discovery step before get_sample/quote_sales, but the exclusions do most of the disambiguation work.

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