get_alternative_signals

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the content categories returned (weather data, political positioning, seasonality, macro events) but omits operational details such as data freshness, caching behavior, authorization requirements, or response structure/format.

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 suffers from marketing fluff ('99% of agents ignore,' 'most tools overlook') that states the same comparative positioning twice. However, the middle sentence efficiently packs four distinct signal categories with specific examples (sunshine effect, Santa Claus rally, FOMC), preventing a lower score.

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 absence of an output schema and annotations, the description partially compensates by enumerating the types of alternative signals returned. However, for a tool returning complex multi-domain data, it lacks details on response structure, nesting, or how these signals are formatted (e.g., boolean flags, numerical scores, calendar events).

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?

The input schema contains zero parameters. Per calibration guidelines, 0 parameters establishes a baseline score of 4. The description appropriately requires no parameter clarification beyond what the empty schema indicates.

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 identifies the resource (unconventional market signals) and distinguishes from siblings by emphasizing 'academically documented patterns most tools overlook.' It lists specific signal categories (weather effects, political cycles, seasonality patterns, macro calendar) that differentiate it from standard tools like get_macro_context. However, it lacks an explicit action verb (e.g., 'Retrieves'), relying on the tool name 'get_' to imply the action.

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 provides implied usage context by characterizing these as signals '99% of agents ignore' and patterns 'most tools overlook,' suggesting when to use this over conventional market data tools. However, it lacks explicit when-to-use/when-not-to-use guidance or named alternative tools to avoid.

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