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

With no annotations, the description carries the burden. It adds context like 'FREE' and 'source-cited', implying a read-only lookup that returns a plain-language verdict with citations. However, it does not disclose exact return format, error behavior, or any side effects, leaving gaps.

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?

A single, tightly written sentence that delivers the essential purpose and scope. Every word contributes, including the 'FREE' qualifier and the parenthetical examples.

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 simple one-parameter tool with no output schema, the description adequately conveys what the tool returns (a sourced, plain-language verdict) and the subject matter (vendor data handling). It doesn't specify the return type explicitly, but the description is sufficient for the tool's simplicity.

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?

The schema fully documents the vendor parameter with an example ('openai-api', 'anthropic-api', 100% coverage). The description adds no parameter-specific detail, so baseline 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 states a clear verb+resource (get a verdict on AI vendor trust) and specifies the scope (data handling categories like trains-on-data, zero-retention, opt-out). It does not explicitly differentiate from sibling tools get_ai_vendor_trust or get_ai_vendor_trust_compare, which would warrant a 5.

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?

No when-to-use guidance is provided. The description does not mention alternatives like the full trust report or comparison tool, leaving the agent to infer usage from the name alone.

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