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

With no annotations, the description carries the burden of behavioral disclosure. It explains the core behavior (splitting on supplied words) and adds the useful caveat to include the domain, but it doesn't describe output shape, matching rules, or edge cases like queries matching no brand terms.

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?

Two short sentences, front-loaded with the purpose and followed by a single crucial usage tip. There is no filler or redundancy; every sentence earns its place.

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 an analytics tool with a rich schema, the description covers the key conceptual trap (brand term variants) and leaves parameter details to the schema. It could optionally describe the output format since no output schema exists, but the definition is adequate 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 coverage is 100%, so the baseline is 3. The description adds meaning to brand_terms by advising users to pass every spelling and include the domain, which goes beyond the schema's example list and clarifies expected input breadth.

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 states a specific, differentiated purpose: splitting search into branded versus non-branded based on user-supplied words. It is clear and actionable, though it doesn't explicitly distinguish itself from siblings like query or top.

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?

It provides clear operational context and a concrete input rule ('Pass every spelling people use, including the domain'). However, it doesn't mention when to prefer this tool over alternatives or when not to use it, so selection guidance remains implied rather than explicit.

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