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veroq_search_suggest

Get autocomplete suggestions for search queries to discover relevant headlines and entities before executing a full search.

Instructions

Get search autocomplete suggestions — matching headlines and entities for a partial query.

WHEN TO USE: To find the right search terms before running veroq_search. Helps discover entities and headlines. RETURNS: Headline suggestions (with category and brief ID) and entity suggestions (with type and mention count). COST: 1 credit. EXAMPLE: { "query": "fed rate" } CONSTRAINTS: Minimum 2 characters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesPartial search query (minimum 2 characters)
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses cost ('1 credit'), return structure ('Headline suggestions... and entity suggestions'), and input constraints ('Minimum 2 characters'). Does not mention idempotency or rate limits, but covers primary behavioral traits for a read-only suggestion tool.

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 structured format with clear headers (WHEN TO USE, RETURNS, COST, EXAMPLE, CONSTRAINTS). Front-loaded purpose statement with zero wasted words. Every sentence earns its place.

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 tool without output schema, description is comprehensive. Compensates for missing output schema by detailing return values. Cost and constraint information complete the picture. No gaps given tool complexity.

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 baseline 3. Description adds value via concrete JSON example ('{ "query": "fed rate" }') and reinforces the minimum length constraint, aiding agent understanding of valid inputs.

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?

States specific verb ('Get') + resource ('search autocomplete suggestions') + scope ('headlines and entities'). Explicitly distinguishes from sibling 'veroq_search' in the WHEN TO USE section, clarifying this is for discovery/preliminary term finding.

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?

Explicitly states when to use ('To find the right search terms before running veroq_search') and names the alternative tool directly. Clear sequential guidance for the agent's workflow.

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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