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

No annotations are present, so the description carries the full burden. It states the tool compares sentiment and returns scores for both, which implies a read-only analysis. However, it omits details like scoring scale, date range, or filtering behavior, leaving some ambiguity.

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 sentence that immediately states the action and output. There is no redundancy or filler; every word contributes value.

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?

The tool is relatively simple, but with no output schema and no annotations, the description only partially covers return semantics. It doesn't mention the sentiment score range, whether brand/competitor are effectively required despite being optional in the schema, or how the language parameter affects 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?

All three parameters are covered by the schema at 100%, and the description adds no extra parameter-level meaning. It doesn't explain score format or missing-parameter behavior, but the schema already documents names and defaults, so the baseline of 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 uses a specific verb ('Compare') and identifies the resource ('news sentiment') and the comparison target (brand vs competitor). This clearly distinguishes it from siblings like sentiment_trend, which would track a single entity.

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 gives clear context: use when comparing brand sentiment against a competitor. It doesn't explicitly mention alternatives or exclusions, but the 'between your brand and a competitor' framing is enough to indicate primary usage.

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