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

The description goes far beyond the readOnlyHint and destructiveHint annotations: it explains the evidence policy (only published rates and aggregate booking actuals from at least 3 organizations), the no-invention guarantee, the coverage 'none' behavior with null ranges, the fallback walk from market to region to country, and the exact output structure. This is exemplary behavioral disclosure.

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 long but every sentence earns its place: purpose, evidence provenance, missing-data behavior, fallback logic, output shape, and parameter caveats. It is front-loaded with the core purpose and structured so an agent can quickly extract the key invariants.

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 complex 9-parameter tool with an output schema, the description covers everything needed to invoke it correctly: what inputs mean, how fallback works, what null results mean, what the response contains (low/typical/high, basis, up to 10 evidence rows), and which parameters currently do not filter. No critical behavioral detail is missing.

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 schema already documents all 9 parameters, including enums and fallback semantics. The description adds some context—notably that event_type, guest_count, and duration_hours are echoed but do not narrow evidence—but this largely repeats what the schema already states, so it meets the baseline without exceeding it.

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 verb and resource: 'Return real market price ranges for live-event services' in a defined geography, with an explicit list of categories (DJ, live band, audio, lighting, etc.). This clearly distinguishes it from siblings like quote_event or search_inventory, which serve different purposes.

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 clear context for when to use the tool: when the agent needs evidence-backed market pricing for live-event services, with a defined fallback chain from market to region to country. It does not explicitly name alternatives or exclusion conditions, but the purpose is specific enough that usage intent is unambiguous.

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