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

Annotations already mark this as read-only and non-destructive, so the description adds value by stating the result ordering (update_date descending) and the empty-array behavior on no matches. It also details the fields returned, giving agents a clear picture of the output.

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 front-loaded with the main action and filter options, but it is long and lists all output fields, which could be trimmed if an output schema were present. It is still organized and each sentence earns its place, though it is not highly concise.

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 tool with six parameters and no output schema, the description covers the filters, output structure, and ordering. It also explains the default browse mode and empty results. It is complete enough for an agent to call it correctly, though pagination behavior is left to the schema.

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 100%, so the schema already documents each parameter. The description adds minor usage hints like the date format for 'since' and partial matching semantics, but does not add significant meaning beyond the schema. Baseline 3 is appropriate.

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 opens with a clear verb and resource: 'Search concrete enterprise AI applications disclosed in US company earnings calls.' It specifies the domain and distinguishes it from sibling search tools by focusing on AI adoption data. The list of output fields further clarifies what the tool returns.

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 states the intended use cases: 'identify where and how a company applies AI, assess deployment maturity, scope, and disclosed value, and inspect the supporting evidence.' It also gives a default behavior for no filters: 'Use no filters to browse the most recently updated observations.' It does not name alternatives, but the context is clear.

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