Retrieve total and marginal incremental ROAS with posterior uncertainty from PyMC-Marketing's incrementality API, replacing ad-hoc LLM ROAS calculations.
Return total and marginal iROAS from PyMC-Marketing's official incrementality API, including posterior uncertainty. No ad-hoc LLM ROAS calculation.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes |
Changes observed during successful MCP inspections.
v0.4.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It discloses the output type (total and marginal iROAS, posterior uncertainty) and clarifies it uses the official API rather than ad-hoc computation. However, it does not mention prerequisites like whether the model must be fitted, how model_id is validated, or any failure/error behavior.
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, front-loaded with the core action and resource, with a clarifying negative statement at the end. Every word earns its place and there is no redundant or vague filler.
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?
This is a low-complexity tool with a single required parameter and no output schema. The description covers what is returned and the source API. It could be slightly more explicit about the model_id's role, but overall it provides enough context for a correct call in most situations.
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 needs to compensate for the model_id parameter. It does not explain what model_id refers to, where to obtain it, or any format constraints. The parameter name is somewhat self-explanatory, but the description adds no semantic guidance 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 returns total and marginal iROAS from a specific official API, and explicitly distinguishes it from ad-hoc LLM-based calculations. This is a precise verb-resource pair that differentiates it from the sibling tools, which focus on CLV, churn, and MMM budgeting.
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 gives clear context: this is the official incrementality API for iROAS, not an improvised calculation. It does not name a specific sibling alternative, but the 'No ad-hoc LLM ROAS calculation' line provides an explicit exclusion and implicitly tells the agent to prefer this API when authoritative iROAS is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.