Allocate marketing budgets under channel or dimension-cell constraints and compare recommended vs baseline posterior responses using a diagnosed model.
Use PyMC-Marketing budget optimization under channel or dimension-cell constraints, then compare baseline and recommended posterior responses. Requires a diagnosed model.
| Name | Required | Description | Default |
|---|---|---|---|
| config | 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 carries the full burden of behavioral disclosure. It mentions the high-level workflow and a diagnostic prerequisite, but it does not disclose whether the operation mutates the model, what the returned comparison looks like, failure modes, or any rate/performance constraints. This is a significant gap for an unannotated tool.
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 two sentences with no filler. The core action is front-loaded, and the prerequisite is placed second. Every sentence earns its place, and the structure is easy to scan.
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 tool has a nested config schema, no output schema, and no annotations, so the description must compensate for missing return and behavior details. It does not explain what the output comparison contains, what conditions could cause errors, or whether the model is modified. An agent would still have significant uncertainty before invoking it correctly.
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 description does not name or explain parameters like budget, constraints, cell_constraints, or planning_periods. It only paraphrases the constraint concept already visible in the schema. Since schema description coverage is reported as 0%, the description needed to compensate by mapping its workflow to config fields, but it does not.
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 specific action and resource: 'PyMC-Marketing budget optimization' with 'channel or dimension-cell constraints', followed by the comparison of baseline and recommended posterior responses. This is clear enough to distinguish it from siblings like simulate_budget or optimize_flighting, though it does not explicitly name a sibling or state what the tool is not.
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 only usage guidance is the prerequisite 'Requires a diagnosed model.' There is no guidance on when to choose this tool over alternatives such as simulate_budget, optimize_flighting, or get_response_curves, and no exclusions or failure-prevention advice. Usage context is mostly implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.