classifier_meta_ensemble

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

With no annotations provided, the description carries full burden. It describes the fusion process and output but does not disclose behavioral traits such as whether it requires authentication, rate limits, computational cost, or if it mutates state. It reads as a read-only operation but this is not explicitly stated.

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 that front-loads the purpose. While it is reasonably concise, the phrase 'fuses every base learner (v3.3 GBM, per-category, DBSCAN anomaly, corroboration)' could be trimmed or moved to avoid clutter.

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 no output schema, the description partially addresses the return value (single probability with per-learner contributions) but does not specify the exact structure or format. For a tool with one parameter, it covers the core function but lacks details on output representation.

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% with the single parameter 'country_code' already well-described. The description does not add any additional meaning or constraints beyond the schema, so a baseline score of 3 is appropriate.

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 it produces a meta-ensemble classifier score for a country, fusing multiple base learners into a single probability. It distinguishes itself from sibling classifiers by claiming it is the broadest single classifier signal available.

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 implies it is the most comprehensive classifier signal but does not explicitly state when to use this tool versus alternatives like classifier_score or classifier_stacking. No guidance on when not to use is provided.

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