Veroq

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

With no annotations provided, the description carries the full disclosure burden. It successfully reveals the cost ('1 credit') and return structure ('Array of entities with name, type, ticker...'), which are critical for agent decision-making. It lacks details on rate limits or data freshness beyond the 24-hour window, preventing a perfect score.

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 uses clear structural headers (WHEN TO USE, RETURNS, COST, EXAMPLE) that front-load critical information. Every line serves a distinct purpose—purpose, usage context, output format, cost, and example—without redundancy or verbose fluff.

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 low complexity (1 optional parameter, simple array output) and lack of output schema/annotations, the description compensates effectively. It documents the return value structure, cost implications, and temporal scope (24h), providing sufficient information for an agent to invoke the tool correctly without external documentation.

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 100% coverage with the 'limit' parameter fully documented as 'Max entities to return.' The description provides a helpful JSON example ('{ "limit": 10 }'), but this primarily illustrates syntax rather than adding semantic meaning beyond what the schema already provides. With high schema coverage, the baseline 3 is appropriate.

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 retrieves 'trending entities' (people, orgs, topics) with mention counts from the 'last 24 hours.' It specifies the verb (Get) and resource effectively. However, it does not explicitly differentiate from the sibling tool 'veroq_social_trending,' which could cause confusion about which trending data source to use.

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 'WHEN TO USE' section explicitly states this is for discovering 'what's dominating the news cycle right now' and identifies it as a 'good starting point for research.' This provides clear contextual guidance, though it lacks explicit exclusions or named alternatives (e.g., when to use veroq_search instead).

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