Run an MCQL report query

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

Adds rich behavioral details beyond annotations: snake_case query vs camelCase response, row structure ('exactly one populated view object'), opt-in requirements, and view constraints. Annotations already cover read-only/idempotent, so these extra details substantially improve transparency.

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 well-structured with 'Tables:' and 'Rules:' sections, and every sentence provides necessary context—no filler. While it lists many table names, each is essential for query construction.

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 query-language tool with no output schema, the description covers available tables, constraints, permissions, row shape, and alternatives. This is a complete and self-sufficient guide for an agent to use the tool effectively.

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 covers all parameters with 100% description coverage, so baseline is 3. The description adds extra query-specific semantics (date range examples, naming conventions, table-specific rules) that go beyond the schema's single example, justifying a 4.

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 it runs MCQL queries via reports:search and lists the available tables. It also distinguishes itself from list_products by explicitly noting this is the way to filter products, making the purpose unambiguous and well differentiated.

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?

Provides explicit usage rules: no SELECT *, performance views require a date range, price_* views require Market Insights opt-in, and it explicitly contrasts with list_products for filtering. These guidelines give clear when-to-use and when-not-to-use context.

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