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veroq_contradictions

Identify conflicting narratives and disputed claims across intelligence briefs to assess risks and verify information accuracy.

Instructions

Find contradictions across intelligence briefs — stories where sources disagree on facts, framing, or conclusions.

WHEN TO USE: To identify conflicting narratives and disputed claims in the news. Useful for risk assessment and due diligence. RETURNS: Array of contradictions with severity, topic, summary, and opposing brief positions (Side A vs Side B). COST: 2 credits. EXAMPLE: { "severity": "high" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
severityNoFilter by severity level (e.g. high, medium, low)
Behavior4/5

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 provides COST ('2 credits') and RETURNS structure ('Array of contradictions with severity, topic, summary...'), compensating for the lack of output schema. However, it omits explicit statements about permissions, idempotency, or caching 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?

The description uses clear structural headers (WHEN TO USE, RETURNS, COST, EXAMPLE) that front-load critical information. Every sentence serves a distinct purpose—defining scope, usage context, output format, billing, and example usage—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 tool's simplicity (1 optional parameter) and lack of output schema, the description is appropriately complete. It compensates for the missing output schema by detailing the return structure, includes cost information critical for an API tool, and provides sufficient context for successful invocation.

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?

Schema coverage is 100%, establishing a baseline of 3. The description adds value by providing a concrete EXAMPLE ('{ 'severity': 'high' }') that demonstrates parameter usage syntax beyond the schema's type description, though it could further clarify if severity values are case-sensitive.

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 explicitly states the tool 'Find contradictions across intelligence briefs' with specific focus on 'stories where sources disagree on facts, framing, or conclusions.' This specific verb+resource combination clearly distinguishes it from sibling tools like veroq_search or veroq_compare.

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

Usage Guidelines4/5

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

The 'WHEN TO USE' section explicitly identifies the context: 'To identify conflicting narratives and disputed claims in the news' and specific use cases like 'risk assessment and due diligence.' While clear about when to use it, it does not explicitly name alternative sibling tools for non-contradiction searches (e.g., veroq_search for general queries).

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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