MCP Apify

by fvegah

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Python

Remote

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TDQS

B3.2/5.0

Scored 2026-04-18 06:26 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.