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

With no annotations, the description carries the full burden. It discloses setup fee, per-record cost, and includes parser dev, QA, and documentation. But it lacks details on turnaround time, data sourcing process, or limitations, making transparency average.

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 relatively concise, containing two sentences plus pricing and tier information. Every sentence adds value, though it could be slightly more structured for readability.

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 custom dataset creation tool with no output schema, the description provides pricing and effort scope but misses process details (e.g., how results are delivered, turnaround time, whether it's a one-time or subscription). It is incomplete for a task of this complexity.

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 50% (schema_description and sources_hint have descriptions, delivery_format and quantity_estimate do not). The description elaborates on schema_description ('Plain English description') and hints at delivery format implicitly, but does not fully compensate for the undocumented parameters.

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 creates novel custom datasets (cross-domain fusion, industry-specific schemas, real-time aggregated intelligence). It distinguishes itself from sibling tools that presumably offer pre-existing datasets.

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 provides context on when to use (novel datasets not existing elsewhere) and includes pricing tiers (Tier 3 for small builds, Tier 4 for novel/large) which helps guide usage. However, it does not explicitly state when not to use or list alternatives.

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