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

With no annotations, the description carries the full burden. It usefully discloses that matching is case-insensitive and across any cell, but says nothing about read-only nature, behavior on zero matches, or whether results are truncated silently at the cap.

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 tight sentence that front-loads the resource and scope. Nothing is wasted or buried.

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?

With no output schema and no annotations, the description should say more about the return shape (rows include which columns? how are they structured?) and truncation behavior. It is minimally adequate but leaves the agent guessing about results.

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 50%: 'query' is documented but 'limit' is not, and the description's 'up to 50' merely restates the schema's maximum of 50. The only real added semantic is that matching is case-insensitive, which the schema omits.

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 (search), resource (rows of the Crypto Exchange Compare HQ dataset), and matching criterion (cells containing the query). It is distinguishable from siblings like dataset_row or dataset_stats, though it never explicitly contrasts itself with them.

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?

There is no guidance on when to use this tool versus alternatives such as dataset_row, dataset_compare, or dataset_top, and no exclusions or prerequisites are stated. Usage must be inferred purely from the one-line behavior.

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