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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety and side-effect behavior are fully covered without the description. The description adds real value by listing the response fields and by flagging the tool as the entry point for id discovery, but with an output schema present the field enumeration is largely redundant.

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?

Two sentences, no filler, and the identity of the resource comes before the usage instruction. The parenthetical inventory of product categories is the only slightly bulky part, but it earns its space by defining the catalog's scope.

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?

With annotations covering the safety profile and an output schema covering return shape, the description needs only purpose, filtering behavior and sequencing, all of which are present. The one material omission is how it differs from list_tracked_tools, which is exactly the ambiguity an agent would hit when choosing among the 14 siblings.

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 67%, so vendor and category are already documented (including a vendor slug example and a category enum), and the description adds little for them. For query it contributes the useful detail that matching is fuzzy with concrete example terms ('copilot', 'devin'), which clarifies matching behavior beyond the schema's terse 'Fuzzy match on name/alias'.

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?

States a concrete resource (catalog of products Havoptic tracks) and enumerates the returned fields (id, name, vendor, category, release count, latest release), which lets an agent know exactly what it gets back. The scope note about what counts as a tracked product (AI coding tools, model/API platforms, agent SDKs, inference runtimes) sharpens it further. It does not, however, distinguish itself from the near-identically named sibling list_tracked_tools, so an agent still has to guess which of the two to call.

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?

'Use first to find valid tool ids' is an explicit workflow instruction that tells the agent when this tool belongs in a sequence, and the fuzzy query examples ('copilot', 'devin') show how to drive it. It stops short of naming an alternative or a when-not-to-use condition, and it never resolves the overlap with list_tracked_tools.

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