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jackpotkeywords_recommend

Provide a product URL or description to run keyword research and receive ranked recommendations by composite Jackpot Score from real Google Ads data.

Instructions

Run the full keyword research pipeline for a product and return ranked keyword recommendations by composite Jackpot Score (volume, CPC, competition, trend, cluster strength, AI relevance). Backed by real Google Ads Keyword Planner data. Costs $0.10 per call (10¢, regardless of limit). Refunded automatically on pipeline failure. Latency ~60–180 seconds — agents should set generous timeouts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoProduct URL to extract context from (e.g., https://yourproduct.com). At least one of url/description required.
descriptionNoPlain-English description of the product (e.g., 'AI keyword research tool for indie makers'). At least one of url/description required.
limitNoMaximum recommendations to return. Default 50, max 200. Cost is flat regardless.
budgetNoOptional daily ad budget in USD. Influences AI scoring/intent classification.
locationNoOptional location for local-intent boosting (e.g., 'San Francisco, CA').
Behavior4/5

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

With no annotations, the description discloses cost, refund on failure, latency, and parameter requirements. It does not detail exact scoring mechanics or behavior when both url and description are provided, but is fairly transparent overall.

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?

Two sentences with no waste: first sentence states core purpose, second adds cost, refund, and latency. Front-loaded and efficient.

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?

No output schema, but description summarizes return content (ranked keywords by composite score components). Includes cost, refund, latency, parameter conditions. Adequate for a single-pipeline tool.

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% but description adds value by noting that limit cost is flat, budget influences scoring, and location boosts local intent. This goes beyond the schema's descriptions.

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 the tool runs the full keyword research pipeline and returns ranked recommendations by composite Jackpot Score. It specifies the verb (run/research/recommend), resource (pipeline), and output (ranked keywords), distinguishing from sibling tools like jackpotkeywords_aeo_scan.

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?

Provides cost ($0.10 flat), refund policy, latency (60-180s), and required parameters (at least one of url/description). However, it does not explicitly state when to use this tool versus the siblings or provide exclusions.

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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