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scrapercity

scrapercity-cli

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scrape_store_leads

Retrieve ecommerce store leads including domains, emails, phones, social profiles, and revenue estimates. Filter by platform, country, category, city, technology, apps, and more. Uses cached database for instant results.

Instructions

Get ecommerce store data (Shopify, WooCommerce, etc). Returns domains, emails, phones, social profiles, revenue estimates. Instant results from cached database.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformNoe.g. "shopify", "woocommerce", "bigcommerce"shopify
countryCodeNoe.g. "US", "GB", "CA"
categoryNoStore category filter
cityNoCity filter
technologiesNoTechnology filter
appsNoInstalled app filter
emailsNoOnly stores with emails
phonesNoOnly stores with phones
instagramNoOnly stores with Instagram
facebookNoOnly stores with Facebook
totalLeadsNoNumber of leads to pull
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses cached database source (non-destructive, fast) but omits rate limits, pagination, or behavior for large totalLeads.

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, front-loaded with purpose and returns, then result source. No redundant information. Efficient and clear.

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?

With 11 parameters and no output schema, description covers return fields. Lacks pagination or limit details, but adequate for common use cases.

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 coverage is 100% with parameter descriptions; description adds general context (e.g., 'instant results') but does not significantly enhance parameter understanding beyond 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?

The description clearly states the tool retrieves ecommerce store data, lists specific data types, and mentions cached instant results. It distinguishes from siblings targeting different data sources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives like scrape_builtwith or scrape_apollo. The description implies usage for ecommerce leads but lacks exclusions or context.

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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