SupplyMaven API Pro

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 effectively describes the tool's behavior: it returns trigger context, predictor value, direction, and narrative paragraphs suitable for reports and briefings. It also implies a read-only, non-destructive operation by focusing on explanations, though it does not explicitly mention permissions, rate limits, or error handling.

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 well-structured and front-loaded, starting with the core purpose and then elaborating on the narrative content and target audience. Every sentence adds essential information without redundancy, making it efficient and easy to understand.

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 tool's complexity (narrative explanations) and lack of output schema, the description is mostly complete. It details what the tool returns (trigger context, predictor value, direction, narrative paragraph) and its use case. However, it could be more complete by specifying output format or limitations, but with no annotations and an empty input schema, it adequately covers the tool's functionality.

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 has 0 parameters with 100% coverage, so the description does not need to compensate for missing parameter documentation. It adds value by explaining the tool's scope (active predictive signals) and the nature of the output (narratives with specific elements), which goes beyond the empty schema. A baseline of 4 is appropriate as no parameters are present.

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's purpose with specific verbs ('Get plain-language explanations') and resources ('active predictive signals'), distinguishing it from siblings like 'get_predictive_signals' by focusing on narratives rather than raw data. It explicitly targets non-quantitative users and explains the content of each narrative (mechanism, economic logic, implications).

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 clear context for when to use this tool: for non-quantitative users wanting to understand the 'why' behind signals without technical details like F-statistics. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as 'get_predictive_signals' for quantitative analysis.

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