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NeuroBlockFoundation

ExtractorLead MCP Server

by NeuroBlockFoundation
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Python
Hybrid

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    TDQS

    A3.9/5.0

    Scored across 13 tools

    Disambiguation3/5

    Most tools are clearly distinct (account, search, single-profile lookup, website analysis), but the surface contains two parallel pipelines that are easy to conflate: preview_bulk_enrichment/start_bulk_enrichment/get_bulk_status/get_bulk_results versus preview_list_enrichment/start_list_enrichment/get_list_enrichment. The descriptions do explain the difference (bulk = discover contacts from a person list, list = fill gaps preserving user columns), but an agent must read carefully to pick correctly. Polling is also split across three different mechanisms (get_task_result, get_bulk_status/results, get_list_enrichment), which adds a second layer of selection risk.

    Naming Consistency5/5

    Every tool follows a consistent snake_case verb_noun pattern (get_account, search_leads, preview_bulk_enrichment, start_bulk_enrichment, get_bulk_results, get_list_enrichment). The bulk/list prefixes create a predictable family structure, and the preview_/start_/get_ prefixes signal lifecycle stage clearly.

    Tool Count5/5

    13 tools is well within the well-scoped 3-15 range. Each tool maps to a real step of the two enrichment workflows plus the lead-search workflow, and none appear redundant at the surface level.

    Completeness4/5

    Coverage is strong: credits/account, lead search, single-profile lookup, website analysis, task polling, and full preview/start/status/results/download lifecycles for both bulk and list enrichment. Minor gaps remain — there is no cancellation tool despite a CANCELLED status being exposed, and no task/job history listing, though agents can work around both.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources