Retrieve response and saturation curves from a fitted PyMC marketing model, returning sampled curve data instead of raw posterior arrays for direct channel analysis.
Return response/saturation information sampled by PyMC-Marketing rather than raw posterior arrays.
| 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?
The description discloses an important behavioral trait: it returns sampled response/saturation information rather than raw posterior arrays. However, with no annotations provided, the description carries the burden and does not mention potential prerequisites, error conditions, or whether any computation is required. The read-only nature is only implied by 'Return.'
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 sentence with no filler and is front-loaded with the action and object. It conveys the key distinction about the output efficiently.
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 one-parameter retrieval tool, the description states the core purpose and output nature, but with no output schema or annotations it leaves the exact return shape, model_id semantics, and relationship to fitted models implicit. It is adequate for selection but not fully self-contained for confident invocation.
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% and the description does not mention model_id or how it selects the model. The parameter name 'Model Id' is somewhat self-explanatory, but the description adds no meaning beyond what the schema already trivially provides.
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 uses a specific verb and resource: 'Return response/saturation information sampled by PyMC-Marketing rather than raw posterior arrays.' It clearly conveys what the tool computes and adds a distinguishing qualifier about the output. It does not explicitly differentiate from sibling tools such as get_posterior_plots, but the core purpose is unambiguous.
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 'rather than raw posterior arrays' implies when this tool is appropriate, but it does not name alternatives or state explicit conditions for selecting this tool over siblings. Usage context is present only by implication.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.