get_attribution

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the tool 'calculates' attribution, without indicating whether it is read-only, what it returns, any side effects, or performance implications. The nature of the operation and its output are left unspecified, which is insufficient for an agent to understand its behavior safely.

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, efficient sentence that directly states the tool's purpose without redundancy. It is front-loaded with the primary action and resource, making it easily scannable. Every word serves a purpose, and there is no unnecessary detail.

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 lacks an output schema, and the description does not explain what the result looks like—whether it returns a detailed breakdown, a single metric, or a comparison of models. It also does not address error handling, data format expectations, or prerequisites. Given the moderate complexity and absent annotations, the description is clearly incomplete for guiding an agent through correct usage.

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?

The description mentions 'various models,' which adds some context to the model parameter, but it does not explain how to select among the five enum options. The channel_points parameter already has a schema description, and the description adds no further meaning there. With 50% schema coverage, the description partially compensates for the undocumented model parameter but remains limited in adding value beyond the schema.

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 calculates multi-touch attribution across marketing channels using various models. It specifies a precise verb and resource, and its unique focus on attribution distinguishes it from sibling tools, none of which mention attribution in their names. An agent can confidently identify its purpose without confusion.

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 does not provide explicit guidance on when to use this tool instead of alternatives; it only states its function. The usage is implied by the purpose (attribution analysis), but there is no mention of exclusions, conditions, or reference to other tools. This meets the baseline for implied usage but does not offer clear direction on model selection or contextual recommendations.

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