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veroq_search

Search verified intelligence briefs by keyword or topic to find specific news, events, or coverage with confidence scores and summaries.

Instructions

Search verified intelligence briefs by keyword or topic.

WHEN TO USE: When looking for specific news, events, or coverage on a topic. Use veroq_ask for natural-language questions instead. RETURNS: Array of briefs with headline, confidence score, category, summary, and brief ID. COST: 1 credit. EXAMPLE: { "query": "NVIDIA earnings", "category": "Technology", "limit": 5 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
categoryNoFilter by category
depthNoSearch depth — fast skips highlights, deep adds entity cross-refs
include_sourcesNoComma-separated domains to include
exclude_sourcesNoComma-separated domains to exclude
limitNoMax results (default 10)
Behavior4/5

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

With no annotations provided, the description carries the full burden and successfully discloses cost behavior ('COST: 1 credit') and return structure ('Array of briefs with headline, confidence score...'). It lacks explicit read-only safety declaration, but 'Search' implies non-destructive behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Uses structured headers (WHEN TO USE, RETURNS, COST, EXAMPLE) to front-load critical information. Every sentence serves a distinct purpose—purpose statement, usage guidance, output contract, pricing, and usage example—with zero redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema, the description compensates by fully detailing the return structure. It also addresses the 50+ sibling tool context by differentiating from 'veroq_ask', and covers the 6-parameter complexity with a concrete example.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While schema coverage is 100% (baseline 3), the description adds value through the EXAMPLE block showing realistic parameter usage patterns (query string format, category values, limit usage) that complement the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific verb (Search), resource (verified intelligence briefs), and mechanism (by keyword or topic). It explicitly distinguishes itself from sibling tool 'veroq_ask' by specifying this is for keyword/topic searches versus natural-language questions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Contains an explicit 'WHEN TO USE' section that defines the specific scenario (looking for specific news/events/coverage) and directly names the alternative tool 'veroq_ask' for natural-language questions, providing clear guidance on tool selection among siblings.

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

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