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scrapercity-cli

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scrape_apollo_filters

Scrape B2B leads from Apollo.io by applying filter parameters such as seniority, industry, or location. Receive results via webhook in 4 days. Requires at least one filter.

Instructions

Scrape leads from Apollo.io using filter parameters instead of a URL. Same 4-day delivery - use webhooks. At least one filter required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seniorityLevelNoe.g. "director", "vp", "c_suite", "manager", "senior"
functionDeptNoe.g. "sales", "marketing", "engineering", "finance"
companyIndustryNoe.g. "computer software", "financial services"
personCountryNoe.g. "United States", "United Kingdom"
personStateNoe.g. "California", "New York"
companyCountryNoCompany HQ country
companyStateNoCompany HQ state
companySizeNoe.g. "11-50", "51-200", "201-500", "501-1000", "1001-5000"
personTitlesNoSpecific job titles to target
companyDomainsNoSpecific company domains
companyKeywordsNoCompany description keywords
personCitiesNoPerson city filter
companyCitiesNoCompany city filter
hasPhoneNoOnly return contacts with phone numbers
countNoNumber of leads (min 500, max 50000)
fileNameNoExport nameApollo Export
Behavior3/5

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

No annotations are provided, so the description must carry the burden. It mentions async delivery ('4-day delivery') and webhooks, but lacks details on failure handling, rate limits, or authentication requirements. The description provides some behavioral context but is not comprehensive.

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?

The description is two sentences with no wasted words, front-loading the core purpose and then adding key constraints. It is optimally concise for the given complexity.

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

Completeness2/5

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

Given 16 parameters, no output schema, and no annotations, the description omits critical context: the async nature is only vaguely hinted, webhook usage is undefined, and there is no guidance on tracking job status or retrieving results. Sibling tools like check_run_status exist but are not referenced. The tool's complexity demands more explanation.

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%, so the schema already describes each parameter. The description adds minimal value beyond stating the filter requirement. It does not explain how parameters interact (AND/OR logic) or validation rules, so it meets the baseline but no more.

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 scrapes leads from Apollo.io using filter parameters, distinguishing it from scrape_apollo which likely uses a URL. The verb 'scrape' and resource 'Apollo.io leads' are specific, and the sibling context confirms differentiation.

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?

The description explicitly requires 'At least one filter' and notes the '4-day delivery' and 'use webhooks', giving clear context. However, it does not explicitly contrast with alternatives like scrape_apollo or query_lead_database, which would improve the score.

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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