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

Annotations already declare read-only and idempotent hints, so the burden is lighter. The description adds valuable behavioral details: the scale of data analyzed, the potential for timeout, and the async workaround. No contradiction with annotations.

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?

Three well-structured sentences. The main function is front-loaded, and each sentence adds value: data sources, inputs/outputs, use cases, and a practical async tip. No fluff or repetition.

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?

Given the tool's complexity, the presence of an output schema, and annotations, the description covers the main purpose, inputs, outputs, data sources, and a performance consideration. It lacks explicit when-not-to-use guidance and doesn't mention include_forums, but overall it is sufficiently complete for an agent to select and invoke the tool correctly.

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 description coverage is only 20% (only async has a description). The description lists key inputs (brand, competitors, time range) and provides some context for their role in the calculation, but it does not detail include_forums or the exact format of time_range. It partially compensates for low schema coverage but not fully.

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 the tool calculates brand equity voice share, specifies the data sources (500K+ news articles and forums from Common Crawl and Wayback Machine), target audience (CMOs), and outputs. This distinguishes it from sibling tools like sentiment_news_pulse or competitor_intel.

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?

Explicitly identifies ideal use cases: competitive benchmarking and brand visibility tracking. It also advises using async:true to avoid timeouts. It does not mention alternatives or exclusion criteria, so it falls short of a 5.

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