Apify Public Data & Leads
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TDQS
Scored across 40 tools
Most tools are clearly distinguished by data source (e.g., app_store vs google_play, linkedin vs glassdoor, sec_edgar vs sec_form_4). The main ambiguity comes from aggregator/subset pairs like company_registry_search vs french_company_search vs gleif_lei_search, and us_business_entity_search vs its state-specific tools, but the detailed 'When NOT to use' and 'Named alternatives' sections resolve this for an agent that reads them.
The large majority follow a snake_case source_subject_search pattern (e.g., epa_facility_search, nhtsa_vehicle_recall_search, linkedin_jobs_search), which is predictable and scannable. A minority use noun-phrase or action names (twitch_live_streams, usaspending_contracts, us_census_geocoder, tech_stack_detector), but there is no camelCase mixing and the overall style remains consistent.
At 40 tools, the set is well beyond the 25+ threshold that reads as too many, and the broad 'public data & leads' purpose does not justify the granular duplication: three contractor-license tools, five business-entity tools, and two overlapping company-registry tools. The count could be meaningfully trimmed by keeping only the multi-state/multi-platform aggregators and dropping the single-state/single-dataset variants.
The set covers a wide swath of public-data categories, including business registries, contractor licenses, jobs, app reviews, biomedical research, government contracting, and security vulnerabilities. However, for a server branded as 'Leads', it lacks general web search, people/contact enrichment, and most US state business registries, leaving notable gaps that agents must work around.