Analyze the ticket dataset

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it emphasizes 'one validated operation,' 'never accepts SQL,' and the need to report returned filters, denominator semantics, and pinned-dataset lineage. This enriches the agent's understanding without contradicting annotations.

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 with high information density. The first sentence front-loads the core functionality and valid operations; the second adds key constraints and reporting requirements. There is no wasted language.

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?

The tool is complex (a discriminated union of five operation types with filters and base_filters), but the input schema is rich and an output schema exists. The description covers the essential operational guidance—quantitative use, no SQL, and reporting lineage/denominator semantics—without needing to repeat schema details. A bit more guidance on selecting between operations or filter constraints would make it fully complete.

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 for the top-level parameter is 0%, so the description must compensate, but it only lists operation names. It does not explain the request object structure, filter fields, group_by semantics, or operation-specific inputs. The sub-schemas have their own descriptions, but from the given context signal, the description itself contributes little to parameter understanding.

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: computing exact statistics on the ticket dataset with a validated set of operations (count, group_count, percentage, distinct_values, top_tags). This specific verb+resource scope, combined with the 'never accepts SQL' constraint and 'quantitative questions' usage, distinguishes it from siblings like search_tickets and get_ticket.

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 directs use for quantitative questions and instructs reporting filters, denominator semantics, and lineage. It also excludes SQL as an input method. It does not explicitly name sibling alternatives or state when not to use the tool beyond the SQL exclusion, so it stops short of a full 5.

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