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

With no annotations provided, the description carries the behavioral disclosure burden. It meaningfully discloses case-insensitive matching, substring-like containment semantics, and the 50-row cap. It does not describe output shape or empty-result behavior, but for a read-only search tool this is reasonably transparent.

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?

The description is a single concise sentence with no filler. It front-loads the core behavior and adds only relevant constraints, making it easy for an agent to parse quickly.

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 two-parameter search tool with no output schema, the description covers the essential contract: what is searched, how matching works, and the result cap. It could optionally mention the default limit or explicitly point to sibling tools, but nothing critical is missing for correct invocation.

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 50%: query is documented but limit is not. The description adds useful context by clarifying that matching is case-insensitive and that results are capped at 50. However, it does not explain the default limit behavior or how limit interacts with the query, so compensation for the schema gap is only partial.

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 clearly specifies what the tool does: returns rows of the Defectbird dataset whose cells contain the query. It adds explicit matching details (case-insensitive, up to 50 rows), which distinguishes it from sibling tools like dataset_row or dataset_stats.

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 intended use is implied: call this when you need rows matching a text query across dataset cells. However, it does not explicitly state when not to use it or which sibling tool to choose instead, so some routing burden falls on the agent.

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