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 key traits: the tool retrieves active policies impacting supply chain risk, includes details like affected GDI pillar and score modifier, and notes that policy adjustments persist as long as effective. However, it lacks information on rate limits, error handling, or data freshness, leaving some behavioral aspects uncovered.

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 followed by clarifying details and usage contexts. Every sentence adds value, such as distinguishing from news alerts and specifying user roles. It could be slightly more concise by combining some clauses, but overall it avoids redundancy and maintains focus.

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 complexity of trade policy impacts and the lack of annotations and output schema, the description is largely complete. It covers purpose, usage, and output details effectively. However, it does not mention potential limitations, such as data sources or update frequency, which could enhance completeness for a tool in this domain.

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 no parameter documentation is needed. The description appropriately focuses on output semantics, detailing what the tool returns ('Each policy includes the affected GDI pillar, score modifier, effective date, and source event'), which adds value beyond the schema. This compensates for the lack of an output schema, making it highly informative for users.

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 active trade policy actions') and resources ('tariffs, sanctions, export controls, import restrictions, and regulatory changes'). It distinguishes from sibling tools by contrasting with 'news alerts that expire after 72 hours' and specifying unique persistence of policy adjustments, unlike tools like 'get_action_signals' or 'get_intelligence_briefs' which may focus on different data types.

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 states when to use this tool by naming specific user roles ('procurement teams navigating tariff exposure, compliance officers tracking sanctions, and supply chain strategists adapting sourcing to policy shifts') and contexts. It implicitly distinguishes from alternatives by highlighting its focus on persistent policy impacts versus time-limited alerts, guiding users away from tools like 'supply_chain_disruption_alerts' for non-policy-related issues.

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