Get Campaign Report

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false), and the description adds substantial context beyond them: the 'unmapped' group guarantee that spend is 'never dropped or guessed into a channel,' the meaning of as_of and source_versions, and the error codes with their HTTP statuses and remediation. This is exactly the extra behavioral detail annotations cannot carry.

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?

Front-loaded with the purpose and sibling contrast, then a Returns block, then an Args block that mirrors the schema. Every sentence carries information, but the return-shape paragraph is partially redundant given an output schema exists, and the Args block, while necessary at 0% coverage, makes the definition long.

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 9-parameter, zero-required aggregation tool this is complete: purpose, metric availability caveats ('where a source fills them'), grouping edge case, return shape, defaults, and error taxonomy are all present. An agent has everything needed to call it correctly without further exploration.

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 0%, so the description must carry all nine parameters, and it does: model_hash (and its dependency for group_by=channel), start/end inclusivity, granularity semantics, group_by values with the 'unmapped' behavior, filters applied before grouping, and a metrics example with default. It adds meaning the schema cannot express, such as what happens without model_hash.

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?

States a specific verb and resource ('report the campaign facts over any window, at any grain') and pins down the exact metric set (spend, media:impressions, media:clicks, platform conversions/value). It explicitly differentiates itself from siblings by naming get_data_report as the same engine over a different fact table, and from get_campaign_incrementality by noting these are 'the PLATFORMS' own attributed numbers, not Simba's incremental attribution.'

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?

Gives clear context for when to reach for this tool (platform-attributed campaign facts reporting) and contrasts it against incremental attribution, which routes the agent away from get_campaign_incrementality. It also documents the error cases and the remediation ('narrow the window or coarsen the granularity'). It stops short of an explicit 'use X instead when…' rule for get_data_report beyond the fact-table distinction.

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