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veroq_entities

Track mentions of specific people, companies, or locations across intelligence briefs to monitor coverage and analyze entity relevance in market data.

Instructions

Get all intelligence briefs mentioning a specific entity (person, company, location, etc.).

WHEN TO USE: When tracking coverage of a specific person, organization, or place across all briefs. RETURNS: Array of briefs mentioning the entity, with headline, confidence, category, and summary. COST: 1 credit. EXAMPLE: { "name": "Elon Musk" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesEntity name to look up
Behavior4/5

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

No annotations provided, so description carries full burden. It successfully discloses cost ('1 credit') and return structure ('Array of briefs... with headline, confidence, category, and summary') which compensates for missing output schema. Could mention rate limits or caching, but covers essential operational constraints.

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?

Excellent structure with clear section headers (WHEN TO USE, RETURNS, COST, EXAMPLE). Every sentence earns its place; no redundant or filler content. Front-loaded purpose followed by operational metadata.

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?

For a single-parameter lookup tool with no output schema, the description is comprehensive. It compensates for missing output schema by detailing return fields (headline, confidence, category, summary) and includes cost information critical for API tool selection.

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 has 100% coverage with 'Entity name to look up', establishing baseline 3. Description adds value by specifying expected entity types (person, company, location) and providing concrete JSON example '{ "name": "Elon Musk" }' that clarifies input format beyond raw schema.

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?

Description opens with specific verb 'Get' + resource 'intelligence briefs' + scope 'mentioning a specific entity', and clarifies entity types (person, company, location). Distinguishes from sibling tools like veroq_search or veroq_brief by emphasizing entity-specific tracking across 'all briefs'.

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?

Explicit 'WHEN TO USE' section provides clear context: 'When tracking coverage of a specific person, organization, or place across all briefs.' This effectively signals the tool's sweet spot vs alternatives like general search, though it doesn't explicitly name sibling alternatives to avoid.

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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