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veroq_research

Conduct deep multi-source research on any topic to produce a structured report with key findings, entity map, and information gaps.

Instructions

Run deep multi-source research on a topic. Produces a structured report with key findings, entity map, and information gaps.

WHEN TO USE: For thorough investigation of a topic requiring analysis across many sources. Use veroq_search for quick lookups instead. RETURNS: Summary, key findings, analysis, confidence assessment, entity map (with co-occurrences), information gaps, and sources used. COST: 3 credits. EXAMPLE: { "query": "impact of AI regulation on semiconductor stocks", "max_sources": 20 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesResearch query
categoryNoFilter by category
max_sourcesNoMaximum sources to analyze
Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It discloses the output format, cost (3 credits), and example usage. It does not mention side effects or permissions, but as a research tool, it is likely read-only and non-destructive. Could add details on auth or rate limits.

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 is concise with six sentences covering purpose, usage guidance, return values, cost, and an example. No extraneous information.

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?

Despite no output schema, the description details the return values (summary, key findings, entity map, etc.) and includes cost. For a multi-source research tool, this provides sufficient context for an agent to understand what to expect.

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

Parameters3/5

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

Schema descriptions cover 100% of parameters, so the description adds little beyond the example. The parameter descriptions in the schema are sufficient but the description does not further clarify semantics or constraints.

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 it performs deep multi-source research and produces a structured report, differentiating it from sibling tools like veroq_search. The verb 'run' and resource 'research' are specific.

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?

Explicit 'WHEN TO USE' section explains it is for thorough investigation and directs to use veroq_search for quick lookups, providing clear guidance on alternatives.

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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