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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds non-obvious context: it returns the same data as the website's table and clarifies that it provides representative entries, which helps set expectations about scope and provenance beyond the structured hints.

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?

Three compact sentences: first identifies the deliverable and its fields, second anchors it to the website table, third routes to related tools. No wasted words and critical information is front-loaded.

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 no parameters and no output schema, the description fully covers what the agent needs: it enumerates every returned field and the data's provenance, and it handles related queries by naming the sibling tools. Nothing crucial is missing for invoking this read-only list tool.

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 tool has zero parameters, and the empty schema already conveys that. The description adds no parameter details because none exist, but it explains exactly what will be returned, which is the only relevant semantic here.

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 the tool returns a table of current SOTA quantum computers with explicit fields (id, hardware type, qubit count, error rate, connectivity, source URL), and distinguishes itself from list_quops_scores and compute_expectation by stating what those provide instead.

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?

Explicitly says to use this for the representative-entry table and directs the agent to compute_expectation for model capability prediction and list_quops_scores for measured scores, so when/where to use vs alternatives is unambiguous.

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