classifier_stacking

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

With no annotations, the description adds some behavioral context by stating it returns stacked probability and base-learner contributions. However, it does not disclose potential prerequisites, permissions, or side effects.

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, front-loading the purpose and adding output detail in the second. Every word is informative, with no redundancy.

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?

Given the tool's complexity (ensemble of multiple models) and lack of output schema, the description adequately explains the return value (stacked probability + contributions). It is complete enough for an agent to understand what to expect.

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 coverage is 100% for the single parameter 'country_code'. The description adds no additional meaning beyond the schema, which already describes it as an ISO 3166-1 alpha-2 code.

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 a stacked ensemble score for a country, combining specific models (GBM, GNN, DBSCAN) and base-learners. It distinguishes itself from siblings like classifier_score or classifier_method by explicitly detailing the ensemble approach.

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?

No guidance on when to use this tool versus alternatives such as classifier_score or classifier_meta_ensemble. The description lacks explicit context for when stacking is appropriate or preferred.

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