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 discloses useful behavior: case-insensitive matching and a maximum of 50 rows. It does not disclose ordering, default limit behavior when omitted, empty-result behavior, or confirm that the operation is read-only, though the name 'search' strongly implies it.

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, compact sentence that front-loads the main behavior and adds the key constraint (case-insensitive, up to 50) without wasted words. Every word contributes to understanding the tool's behavior.

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 with two parameters and no output schema, the description is mostly complete: it specifies what is searched, the matching rule, and the result cap. It is slightly lacking on default limit behavior and result ordering, but these are minor gaps given the tool's simplicity.

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 only the query parameter; the description adds case-insensitive matching semantics for query and clarifies the 'up to 50' cap tied to limit. It enriches the schema's sparse documentation, though it does not state the default limit when the optional limit parameter is absent.

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 states that the tool searches the Pickpathly dataset for rows containing the query string in any cell, with case-insensitive matching. It communicates the core filtering behavior and result cap. It does not explicitly differentiate from siblings like dataset_row or dataset_top, but the resource and action are evident.

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 this tool is for free-text substring search across cells, which gives the agent a sense of when it applies. However, it provides no explicit guidance on when to prefer this over sibling tools such as dataset_columns or dataset_compare, and no exclusions or alternative conditions are mentioned.

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