MCP 服务器

MCP Apify

Enables AI assistants to interact with the Apify platform to manage actors, monitor runs, and retrieve scraped data from datasets. It supports natural language commands for executing web scrapers, managing tasks, and accessing key-value stores.

README

MCP Apify

A Model Context Protocol (MCP) server for the Apify platform. This server enables AI assistants like Claude to interact with your Apify account — managing actors, monitoring runs, retrieving datasets, and more.

Features
Prerequisites
Installation
Configuration
Usage
Available Tools
Examples
Development
License

Features

Actors — List and inspect actors in your account
Runs — Start, monitor, abort, and resurrect actor runs
Tasks — Manage and execute saved actor configurations
Datasets — Retrieve scraped data and results
Key-Value Stores — Access stored records and outputs
Schedules — Monitor automated execution schedules

Prerequisites

Python 3.10 or higher
An Apify account
An Apify API token (get it here)

Installation

Option 1: Install from source

git clone https://github.com/fvegah/mcp-apify.git
cd mcp-apify
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

Option 2: Install with uv (recommended)

git clone https://github.com/fvegah/mcp-apify.git
cd mcp-apify
uv venv
source .venv/bin/activate
uv pip install -e .

Configuration

Step 1: Set your API token

Add your Apify API token to your shell profile (~/.zshrc, ~/.bashrc, or equivalent):

export APIFY_API_TOKEN="apify_api_xxxxxxxxxxxxxxxxxxxxx"

Reload your shell configuration:

source ~/.zshrc  # or source ~/.bashrc

Step 2: Configure your MCP client

Claude Desktop

Add the following to your Claude Desktop configuration file:

OS	Path
macOS	`~/Library/Application Support/Claude/claude_desktop_config.json`
Windows	`%APPDATA%\Claude\claude_desktop_config.json`
Linux	`~/.config/Claude/claude_desktop_config.json`

{
  "mcpServers": {
    "apify": {
      "command": "/absolute/path/to/mcp-apify/.venv/bin/python",
      "args": ["-m", "mcp_apify.server"]
    }
  }
}

Important: Replace /absolute/path/to/mcp-apify with the actual path where you cloned the repository.

Claude Code (CLI)

Add to your Claude Code MCP settings (~/.claude/settings.json):

{
  "mcpServers": {
    "apify": {
      "command": "/absolute/path/to/mcp-apify/.venv/bin/python",
      "args": ["-m", "mcp_apify.server"]
    }
  }
}

Step 3: Restart your MCP client

After configuration, restart Claude Desktop or Claude Code to load the new MCP server.

Usage

Once configured, you can interact with Apify through natural language. The AI assistant will use the appropriate tools automatically.

Example prompts

"List my recent actor runs"
"Show me the status of run abc123def456"
"Get the results from my last web scraper run"
"Abort the currently running actor"
"Run the apify/web-scraper actor with URL https://example.com"
"Show me all my scheduled tasks"

Available Tools

User Information

Tool	Description
`get_user_info`	Get information about the authenticated user

Actors

Tool	Description
`list_actors`	List all actors (created or used by user)
`get_actor`	Get details of a specific actor

Runs

Tool	Description
`list_actor_runs`	List runs for a specific actor
`list_user_runs`	List all runs across all actors
`get_run`	Get details of a specific run
`get_last_run`	Get the most recent run of an actor
`run_actor`	Start a new actor run with optional input
`abort_run`	Stop a running actor execution
`resurrect_run`	Restart a finished run
`get_run_log`	Retrieve the log output of a run

Tasks

Tool	Description
`list_tasks`	List all saved actor tasks
`get_task`	Get task configuration details
`run_task`	Execute a task with optional input override
`list_task_runs`	List runs for a specific task
`get_task_last_run`	Get the most recent task run

Datasets

Tool	Description
`list_datasets`	List all datasets
`get_dataset`	Get dataset metadata
`get_dataset_items`	Retrieve items from a dataset
`get_run_dataset_items`	Get items from a run's default dataset

Key-Value Stores

Tool	Description
`list_key_value_stores`	List all key-value stores
`get_key_value_store`	Get store metadata
`list_keys`	List keys in a store
`get_record`	Retrieve a specific record
`get_run_output`	Get the OUTPUT record from a run

Schedules

Tool	Description
`list_schedules`	List all schedules
`get_schedule`	Get schedule configuration
`get_schedule_log`	Get schedule execution history

Examples

List recent runs with status filter

Ask: "Show me my failed runs from the last week"

The assistant will use list_user_runs with status: "FAILED" to retrieve the information.

Run an actor with custom input

Ask: "Run the web scraper on https://news.ycombinator.com and wait for results"

The assistant will:

Use run_actor with the appropriate input
Use get_run with wait_for_finish to monitor completion
Use get_run_dataset_items to retrieve the results

Monitor a running actor

Ask: "What's the status of my current scraping job?"

The assistant will use list_user_runs with status: "RUNNING" to find active runs.

Development

Project structure

mcp-apify/
├── pyproject.toml              # Package configuration
├── README.md                   # This file
├── .gitignore
└── src/
    └── mcp_apify/
        ├── __init__.py
        ├── client.py           # Apify API client
        └── server.py           # MCP server implementation

Running locally

# Activate virtual environment
source .venv/bin/activate

# Run the server directly (for testing)
python -m mcp_apify.server

Running tests

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

API Reference

This MCP server wraps the Apify API v2. For detailed information about request/response formats and available parameters, refer to the official documentation: