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MCP Apify

by fvegah
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Python
Remote

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    • Apify MCP Server

      jirispilka
      A
      license
      Not graded
      quality
      D
      maintenance
      Connects AI assistants to thousands of Apify Actors for web scraping, data extraction, and automation tasks, with dynamic tool discovery to find and use any Actor from the Apify Store in real time.
      25,052 npm
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    • AXE Fleet MCP Server

      memjar
      A
      license
      Not graded
      quality
      Not graded
      maintenance
      Exposes over 19,000 Apify Actors as MCP tools for web scraping, data extraction, and OSINT automation. It enables AI agents to dynamically discover and execute scrapers to collect structured data and crawl web content.
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    • Apify Model Context Protocol

      ampcome-mcps
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to use Apify Actors as tools to perform specific tasks like web scraping, data extraction, and searching across various platforms through the Model Context Protocol.
      25,052 npm
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    • Web Intelligence MCP Server

      ErnestaLabs
      F
      license
      Not graded
      quality
      D
      maintenance
      Provides AI agents with real-time web capabilities including live search results, markdown web scraping, business lead generation, and detailed company information. It enables agents to bypass knowledge cutoffs by accessing current web data through a monetized Apify Actor.
      -
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.2/5.0

    Scored across 28 tools

    Disambiguation4/5

    Most tools are clearly distinct by resource (actor, dataset, run, etc.) and action (get, list, run), but some overlap exists, such as get_run_dataset_items and get_dataset_items, which could cause confusion about which to use for dataset access. The descriptions help clarify, but the boundaries between run-specific and general dataset tools are not perfectly sharp.

    Naming Consistency5/5

    Tool names follow a highly consistent verb_noun pattern throughout, with verbs like get, list, run, abort, and resurrect applied predictably to nouns like actor, dataset, run, etc. There are no deviations in style or convention, making the naming scheme clear and systematic.

    Tool Count3/5

    With 28 tools, the count feels heavy for the domain of managing Apify actors, datasets, runs, and related resources. While it covers many operations, it may be overwhelming for agents, as some tools could be consolidated or omitted without losing functionality, such as having multiple get_* variants that might be redundant in practice.

    Completeness5/5

    The tool set provides comprehensive CRUD and lifecycle coverage for the Apify domain, including operations for actors, datasets, key-value stores, runs, schedules, and tasks. It supports creation (run_actor, run_task), retrieval (get_*, list_*), updates (resurrect_run), and deletion (abort_run), with no obvious gaps that would hinder agent workflows.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive

    Resources