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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond those annotations: the catalog is 'SYNTHETIC and illustrative,' published for line-by-line quote checking, and explicitly not a price list any organization charges. This prevents a critical misuse of the data.

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 three sentences with no wasted words: first the trigger conditions, then the catalog contents, then the synthetic-data warning. The most decision-relevant information is front-loaded, and every sentence earns its place.

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?

Given no parameters, a read-only annotation profile, and the presence of an output schema, the description is complete for an agent to select and invoke the tool correctly. It explains what the tool returns conceptually, why it exists, and the critical caveat that the data is synthetic, leaving no obvious gap for correct usage.

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 schema is empty, so there are no parameter semantics to document. The description compensates by clarifying what content the tool exposes—event profiles, production tiers, add-ons, market indexes, and rules—which gives the agent a useful mental model of the catalog scope.

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 opens with a specific use case—'when someone asks how an event quote was priced, or wants typical live-event production rates'—and names the resource: the public Soundcheck pricing catalog behind quote_event. It clearly differentiates this browsing/listing tool from the quote_event tool it supports, and the SYNTHETIC caveat removes ambiguity about the data's real-world status.

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 explicitly states when to use the tool: to check how a quote was priced or to find typical production rates by city. It also identifies quote_event as the related tool whose catalog this browses. However, it does not explicitly name alternative siblings like lookup_market_pricing or state a direct 'use X instead' rule, so guidance is strong but not exhaustive.

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