report_issue

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

With no annotations, the description carries the behavioral disclosure burden. It reveals the key side effect: 'Files a ticket to the verification queue,' clarifying that the tool does not directly edit data but queues it for review. This is meaningful context beyond the schema, though other potential behaviors (e.g., auth requirements, response format) are not addressed.

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 efficient: two sentences that front-load the purpose, provide concrete examples, and state the usage condition. There is no filler or redundant restating of schema fields.

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 relatively simple four-parameter tool with 100% schema coverage, the description covers purpose, usage condition, and the core behavioral outcome (verification queue ticket). It does not describe the response/return value, but the schema and the 'files a ticket' statement provide enough context for correct invocation. Slightly more detail about what happens after filing would make it completely self-contained.

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 100%, so the baseline is 3. The description adds extra semantic value by mapping issue examples to the field enum ('wrong price' → pricing, 'dead/renamed tool' → status, 'missing or incorrect feature' → feature) and by referencing evidence from the vendor's own site, which aligns with the evidence_url parameter.

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's purpose: 'Report a data-quality issue' with concrete examples ('wrong price, dead/renamed tool, missing or incorrect feature'). It also identifies the resource (Toolradar data) and differentiates from sibling tools that search, compare, or list tools, none of which handle issue reporting.

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 an explicit when-to-use condition: 'Use this when Toolradar data contradicts what you observe on the vendor's own site.' It does not explicitly name alternatives or state when not to use the tool, but the condition is clear and the sibling tools are functionally distinct enough that an agent can route correctly.

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