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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by revealing the internal derivation of trendNorm, the critical external-source requirement for externalVolumeNorm, the peak<50 zeroing rule, and the optional exponential freshness decay with a 90d half-life. It does not describe error conditions or return format beyond the 0-25 range, but the computational behavior is richly disclosed.

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 dense but efficient; every clause adds important information. The formula is front-loaded, followed by a critical caveat, then internal derivation, a threshold rule, and the optional decay behavior. It could be slightly more structured, but it is not padded and each 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 a computation tool with no output schema and no annotations, the description covers the essential behavioral context: the formula, the source constraint, normalization handling, an edge-case threshold, and decay logic. The main missing piece is guidance on when to choose v2 over v1, but for invoking the tool correctly with the given inputs, the description is largely complete.

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, but the description adds meaningful semantics beyond the schema: it warns that externalVolumeNorm must NOT use Trends data to avoid double-counting, clarifies that trendNorm is derived internally from trendsTimelineValues, and explains the 90-day half-life for daysSinceLastSignal. This goes well beyond what the schema descriptions provide.

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 computes a 'Search velocity' score on a 0-25 scale and provides the exact weighted formula (0.40*volume + 0.30*trend + 0.20*intent + 0.10*geo). This distinguishes it as a computation tool with a precise output and makes its purpose concrete rather than merely restating the name.

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 strong input-handling guidance (e.g., externalVolumeNorm must come from external sources, not Trends), but it never states when to use this tool versus the sibling compute_search_velocity or any other alternative. Given that a v1 sibling exists, some explicit differentiation or selection criteria would be valuable.

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