Fit a Gamma-Gamma Bayesian model to estimate customer monetary value from repeat purchase frequency and average transaction value, providing posterior distributions for future spending.
Fit a Bayesian monetary value transaction model (Gamma-Gamma) on repeat customer spending. Requires frequency and average monetary value columns.
| 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 present, so the description carries the full burden. It discloses that this is Bayesian and Gamma-Gamma, but not what happens on invocation—whether a model artifact is saved, how long MCMC runs, or what output is returned. Sampler settings exist in the schema, but the description does not explain their behavioral effect.
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 one efficient sentence with two useful clauses. No filler or redundant restatement of the tool name. It could be slightly more structured by front-loading prerequisites, but overall it is compact.
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 fitting tool with no annotations and no output schema, key operational details are missing: dataset_id is required, a registered dataset must exist, and the return/result behavior is unspecified. It also does not signal how sampler configuration affects runtime. The description is not complete enough to invoke correctly from context alone.
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% at the top-level config parameter, so the description must compensate, but it only references frequency and monetary value columns. It does not mention required dataset_id, model_type constraint, or sampler configuration. The schema's nested descriptions carry most of the burden.
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?
Description opens with a specific verb and resource: 'Fit a Bayesian monetary value transaction model (Gamma-Gamma) on repeat customer spending.' This distinguishes it from frequency/purchase siblings by naming the value model explicitly. It does not name a sibling, but the model type and subject make the tool's role clear.
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?
It states required inputs ('Requires frequency and average monetary value columns'), which is a clear precondition. It does not state when to prefer this over fit_purchase_model or fit_clv_model, nor any exclusions. The usage context is implied by the model type.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.