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

With no annotations, the description carries the full behavioral burden. It usefully discloses case-insensitive matching semantics, but says nothing about result limits, ordering, behavior when the column name is invalid, or whether the dataset/column must be pre-existing.

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?

A single front-loaded sentence with the dataset named first and no wasted words. It is a verbless fragment ('The rows of...where...'), which is terse but slightly awkward as a standalone instruction.

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 two-parameter lookup with no annotations, no output schema, and 0% schema coverage, the description is thin — it omits return shape beyond 'rows', ordering, empty-result behavior, and column-name validity, all of which an agent needs 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?

Schema description coverage is 0%, so the description must compensate, and it only partly does: 'column equals a value' tells the agent the two required params are a column identifier and a comparison value, but not that 'column' must be an existing Wen Receipts column name nor what happens on mismatch.

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 names a specific resource (rows of the Wen Receipts dataset) and the precise retrieval condition (column equals value exactly, case-insensitive). It is clear what the tool returns, though it never names a sibling such as dataset_search to contrast exact-match against fuzzy retrieval.

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: the emphasis on 'exact' and 'case-insensitive' signals this is for lookups when the key value is already known, in contrast to a search-style tool. However, no alternative tool is named and no conditions for when-not-to-use are given.

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