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

With no annotations, the description carries the full load. It usefully discloses case-insensitive matching, 'any cell' coverage, and the 50-row cap. But it omits ordering, the behavior when limit is omitted, and what happens when no rows match.

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 one concise sentence that front-loads the core behavior, then adds the two most important constraints: case-insensitivity and the row cap. No filler or redundant restatement of the tool name.

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 search tool, the description covers the target dataset, the matching rule, and the result cap. It is sufficiently complete for an agent to invoke it correctly, though a small amount of detail about default limit or ordering would make it fully airtight.

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 schema describes 'query' but not 'limit'. The description adds meaningful context by clarifying case-insensitive cell-content matching and the up-to-50 cap, but it does not explain the default limit or pagination behavior beyond what the schema already constrains.

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 identifies the tool as returning rows of the Funnelvo dataset that contain the query in any cell, which is a specific and searchable operation. It does not explicitly contrast it with sibling tools like dataset_top or dataset_row, so it falls short of full differentiation.

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 tool when you need to find rows by cell text. However, the description provides no explicit guidance on when to prefer it over the sibling dataset tools or what it should not be used for.

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