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

With no annotations, the description must carry the behavioral burden. It discloses case-insensitive matching and a 50-result cap, which is useful, but it does not explicitly confirm the operation is read-only or describe ordering, pagination, or empty-result behavior.

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 no filler. Every clause adds meaningful information: rows, dataset, cell matching, case-insensitivity, and result cap.

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 2-parameter search tool with no output schema, the description covers the essential invocation details: what to search, how matches are determined, and the maximum result count. Minor gaps remain around return row shape and ordering, but these are unlikely to prevent correct usage.

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 coverage is only 50%, but the description partially compensates: the query is clearly the search text and the 50-row statement explains the intent of the limit parameter. It also adds the case-insensitive detail, which enhances query semantics 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?

The description states a precise behavior: return rows of the Binstockly dataset where any cell contains the query. It adds case-insensitivity and a 50-row cap, going well beyond the title and clearly distinguishing this from siblings like dataset_stats or dataset_columns.

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 implies usage: when you need to find rows by arbitrary text across any cell. However, it gives no explicit guidance about when not to use it or which sibling tool to choose instead, leaving the decision to inference.

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