attribution_model

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

Despite no annotations, the description provides comprehensive behavioral details: read-only, stateless, idempotent, rate limits (10/day free, unlimited pro), and no auth required. This fully informs the agent about side effects and constraints.

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 clear sections (Args, Behavior, When to use/not use) and is concise without extraneous text. Every sentence adds value.

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 tool has 3 parameters, no output schema, and no annotations, the description covers key aspects: purpose, parameters, behavior, usage boundaries. It lacks explicit mention of output format but the behavior section implies analysis output. Overall, it is nearly 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?

With 0% schema description coverage, the description compensates well for two of three parameters: touchpoints (example format) and model (enum list). However, the api_key parameter is not mentioned at all, missing an opportunity to clarify its optional nature.

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 function: "Apply an attribution model to marketing touchpoints" and "Distributes conversion credit across channels based on the chosen model." It specifies verb (apply), resource (attribution model to touchpoints), and outcome. Siblings like 'ab_test_analyze' and 'campaign_roi' are distinct, so no 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 includes explicit 'When to use' and 'When NOT to use' sections, advising against real-time production use without review. However, the 'When to use' is generic ('structured analysis or classification') and does not contrast directly with sibling tools, which would improve guidance.

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