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

With no annotations, the description carries full burden. It discloses model version, feature count, grid cell size, premium pricing, and validation metrics. No mention of rate limits or auth needs, but sufficient for a read-only prediction tool.

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?

Two sentences, front-loaded with core functionality, followed by use cases and pricing. Every sentence adds value 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?

Covers model, output probabilities, grid cell, use cases, and pricing. Lacks return format details, but output schema is absent; description sufficiently explains output for the tool's purpose.

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 clear parameter descriptions. The description adds no additional meaning to the parameters beyond their schema definitions, meeting the baseline.

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 provides 72-hour earthquake probability forecasts using a specific ML model, and lists the magnitude thresholds. It differentiates from sibling 'alya_seismic_recent' (which likely deals with recent quakes) by specifying it's a forecast tool.

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?

Explicit mention of use cases (risk assessment, insurance pricing, surfacing high-risk regions) and warning that it's premium not a public scrape. However, no explicit when-not-to-use or comparison with sibling tools.

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