electional_aspect_search

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

Annotations already declare the tool as read-only and non-destructive. The description adds valuable behavioral details: returns specific data fields, costs 2 credits per call, and includes a usage example. No contradictions with annotations.

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 extremely concise: three sentences plus an example. It front-loads the core purpose, includes essential details (credit cost, example), and contains no filler or redundant information.

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 lists what is returned, explains the use case, and provides a credit cost. It does not detail domain-specific concepts like 'quality score' interpretation, but the schema covers parameters. Overall sufficient for a simple query tool.

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?

Input schema has 100% description coverage, so each parameter is already documented. The description provides an example of using the 'date' parameter but adds no new semantic meaning for 'max_orb' or 'aspects' beyond the schema.

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 finds active aspects at a specific moment, lists return fields (aspect type, orb, quality score, applying/separating), and provides an example. It distinguishes from siblings like ephemeris_aspect_check by focusing on a single moment ('weather of a given day') rather than time ranges.

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 explicitly recommends use for checking the 'weather' of a day and gives a concrete example. However, it does not mention when to avoid this tool or name specific alternatives among the many aspect-related siblings.

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