data_query

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

With no annotations, the description carries the full burden of disclosing behavior. It only states that the tool 'executes' a query, with no mention of whether the query is read-only, what the response format is, whether results are returned directly or via an ID, or any side effects. This is a significant transparency gap for a query execution tool.

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 a single, front-loaded sentence that efficiently communicates the core action. There is no fluff, and the key inputs are listed early. However, it is so brief that it sacrifices informative value, but this dimension specifically rewards economy and clarity, which this achieves.

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 the complexity (5 parameters, nested objects, no output schema), the description is incomplete. It does not explain how the parameters interrelate, what the returned data looks like, or how the tool fits into the overall query workflow beyond the implied 'selected' inputs. The sibling tool names suggest a workflow but are not sufficient documentation for effective use.

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 coverage is 0%, so the description must compensate. It mentions source, accounts, fields, and date range, giving a high-level hint of their roles, but omits the filters parameter entirely. It doesn't describe expected formats, structures, or constraints for any parameter, leaving the agent to guess.

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 identifies the tool as executing a marketing analytics query with specific inputs (source, accounts, fields, date range). This verb-resource pairing is specific and distinguishes it from sibling discovery tools (data_source_discovery, accounts_discovery, field_discovery) and the results retrieval tool (get_query_results), as this is the step that actually runs the query.

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 implies a workflow: use this after selecting source, accounts, and fields (likely via the discovery tools) and to execute a query. However, it does not explicitly state when to use this tool versus get_query_results or provide exclusions. The context is clear but not explicit enough for a higher score.

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