Lord of Prediction

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

Annotations already declare readOnlyHint=true, so the tool is read-only. The description adds no further behavioral context—no mention of how selection is computed, any prerequisites, or output characteristics.

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?

Extremely brief but lacks structure. The example request body is incomplete and not helpful. The description would not be considered efficient; it is under-specified.

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?

With no output schema and minimal description, the tool lacks context on return values, interpretation, and what 'predictive significator selection' entails. The schema partially covers input, but the overall completeness is low.

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%, so the schema provides parameter details. The description offers only a partial example request body ('{"...chart":"..."}'), which adds little beyond the schema. Baseline 3 applies.

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 states 'Predictive significator selection,' which indicates a verb and resource. However, it is vague and does not differentiate from numerous siblings like 'hellenistic_schmidt_levels_of_causation' or 'hellenistic_schmidt_aspectual_typology', leaving ambiguity about what exactly this tool selects.

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. The group label '[Group: Hellenistic — Schmidt tradition]' is insufficient to distinguish usage from the many hellenistic_schmidt_* siblings.

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