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discover_variants

Generates multiple query variations from a narrow seed query to expand discovery search. Returns a prompt for variant generation.

Instructions

Emit a query-variation prompt for discovery search. Generates alternate discover_new queries. When to use: before discovery when the seed query is narrow. When NOT to use: to run search; use discover_new instead. Args: cluster_slug: context; query: seed query; count: variant target. Returns: keys prompt, target_count, error. Example: >>> discover_variants("my-topic", "LLM agents", count=4) {"target_count": 4, "prompt": "..."}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_slugYes
queryYes
countNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Describes return format (keys prompt, target_count, error) and provides an example, but could elaborate more on side effects or safety. However, given no annotations, the description sufficiently discloses 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?

Very concise with clear sections (description, when to use, args, returns, example) and front-loaded purpose. No wasted words.

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

Completeness4/5

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

Provides sufficient information for a simple tool: purpose, usage conditions, parameter semantics, and return format. Lacks output schema details but example compensates.

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?

Adds meaningful context to parameters beyond the input schema: cluster_slug as context, query as seed, count as target count. This compensates for the 0% schema description coverage.

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 emits query-variation prompts for discovery search and distinguishes itself from sibling tool discover_new by specifying that discover_new should be used to actually run search.

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 provides when to use (before discovery with narrow seed query) and when not to use (to run search; use discover_new instead), offering 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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