get_channel_contributions

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

With no annotations, the description carries the behavioral burden. 'Does not fabricate estimates' adds a useful transparency guarantee that outputs are model-derived rather than invented, and 'fitted model' signals a prerequisite. However, it does not disclose output format, error behavior, or what happens when model_id is invalid or the model is not fitted.

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?

Two short sentences with no filler; the core action is front-loaded and the non-fabrication guarantee earns its place. The length is appropriate for a simple one-parameter tool.

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 definition is adequate for selecting the tool but not fully complete for invoking it blind: there is no output schema, no return-structure details, and no guidance on where model_id comes from. Complexity is low and the parameter is obvious, so the gaps are moderate rather than severe.

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 input schema has a single required model_id with no description (0% coverage), so the description must compensate. 'From the fitted PyMC-Marketing model' implies model_id identifies a previously fitted model, adding some meaning beyond the schema. It does not explain how to obtain or format model_id, leaving partial compensation.

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 names a precise operation ('Return posterior channel contribution summaries') and scope ('from the fitted PyMC-Marketing model'), which clearly distinguishes it from sibling tools like get_response_curves or optimize_budget. There is no ambiguity about the resource being operated on.

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 phrase 'from the fitted PyMC-Marketing model' implies the tool should be used after fitting a model, but it never explicitly states when to choose this over related tools like get_incremental_roas, get_response_curves, or get_posterior_plots. No alternatives or exclusions are given.

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