get_attribution_model

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

No annotations are provided, and the description offers no behavioral context beyond stating it retrieves details. It does not disclose read-only nature, permissions needed, potential errors, or side effects, leaving a significant gap for safe invocation.

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 concise sentence that lists key return elements. It is front-loaded and contains no extraneous information, though a slightly more structured format (e.g., bullet points) could improve readability.

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 simple tool with one parameter and no output schema, the description sufficiently covers what the tool returns. It lacks mention of error states or authentication, but given low complexity, it is largely 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?

Schema coverage is 100% (description for attribution_id), and the description does not add extra meaning beyond what the schema already provides. The parameter's purpose is clear from the schema, so the description is adequate but not enhanced.

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 retrieves full details of a specific attribution configuration and enumerates the included components (model, window, conversion events, cross-device settings, intended use cases), distinguishing it from sibling tools like list_attribution_models and compare_attribution_models.

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 explicitly provide when to use this tool versus alternatives. Usage is implied from the context of sibling tools (e.g., having a specific ID vs. listing), but no direct guidance or exclusions are stated.

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