What can @apify/actors-mcp-server do?

mcp server instantiation for apify actors, actor discovery and tool schema generation, actor execution and result streaming, parameter validation and schema enforcement, apify api credential management and authentication, mcp protocol compliance and message routing, error handling and failure recovery, configuration and environment setup

@apify/actors-mcp-server

MCP ServerFree

Apify MCP Server

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

mcp server instantiation for apify actors

Medium confidence

Bootstraps a Model Context Protocol server that exposes Apify Actor APIs as MCP tools, implementing the MCP server specification to translate HTTP-based Actor endpoints into standardized tool schemas. Uses the @modelcontextprotocol/sdk to handle MCP protocol negotiation, tool registration, and bidirectional message routing between MCP clients (Claude, other LLMs) and Apify's Actor execution platform.

Solves for

I want to connect Claude or another LLM to Apify Actors so it can trigger and manage web scraping tasksI need to expose my Apify Actors as callable tools in an AI agent without building custom API wrappersI want to standardize how my LLM interacts with Apify through the MCP protocol instead of direct HTTP calls

Best for

AI agent developers building automation workflows that require web scraping or data extraction

Teams using Claude with MCP clients who want native Apify integration

Developers migrating from REST API calls to standardized MCP tool calling

Requires

Node.js 16+

Apify API token (available from Apify account settings)

@modelcontextprotocol/sdk package

Limitations

Requires running a separate Node.js process to host the MCP server — adds deployment complexity vs direct API calls

MCP protocol overhead adds ~50-100ms latency per tool invocation compared to direct HTTP

Limited to tools exposed by Apify's Actor API — cannot wrap custom business logic not available through Actors

What makes it unique

Implements MCP server specification specifically for Apify's Actor platform, translating Actor HTTP APIs into standardized MCP tool schemas with automatic schema generation from Actor input/output definitions

vs alternatives

Provides native MCP integration for Apify Actors without custom wrapper code, whereas direct HTTP calls require manual schema definition and lack MCP protocol standardization

actor discovery and tool schema generation

Medium confidence

Automatically discovers available Apify Actors in a user's account and generates MCP-compliant tool schemas by introspecting Actor input specifications and output formats. Queries the Apify API to fetch Actor metadata, parses input/output JSON schemas, and converts them into MCP ToolDefinition objects with proper parameter typing, descriptions, and validation rules.

Solves for

I want my LLM to automatically discover which Actors I have available without manually defining tool schemasI need the MCP server to generate accurate parameter schemas from my Actors' input definitionsI want to ensure the LLM understands what inputs each Actor accepts and what outputs it produces

Best for

Teams with multiple Actors who want dynamic tool discovery without hardcoding schemas

Developers building multi-tenant systems where Actor availability varies per user

Organizations that frequently add or modify Actors and need schema updates without redeployment

Requires

Apify API token with read access to Actor metadata

Actors must have properly defined input/output JSON schemas in their configuration

Network connectivity to Apify API (api.apify.com)

Limitations

Schema generation depends on Actor developers providing complete input/output JSON schemas — incomplete schemas result in poor LLM understanding

Discovery latency scales with number of Actors in account — accounts with 100+ Actors may experience 2-5 second discovery delays

No caching of discovered schemas — each server restart triggers full re-discovery, adding startup time

What makes it unique

Performs dynamic schema generation by parsing Apify Actor input/output definitions and converting them to MCP ToolDefinition format, enabling zero-configuration tool exposure without manual schema authoring

vs alternatives

Eliminates manual schema definition compared to generic MCP servers, automatically staying in sync with Actor configuration changes

actor execution and result streaming

Medium confidence

Executes Apify Actors through the MCP protocol by translating tool calls into Actor run requests, managing the execution lifecycle (queuing, running, completion), and streaming results back to the MCP client. Handles asynchronous Actor execution by polling the Apify API for run status, buffering intermediate results, and returning final outputs in MCP-compatible format with error handling and timeout management.

Solves for

I want to trigger an Actor run from Claude and wait for results without polling manuallyI need to pass parameters from an LLM tool call directly to an Actor's inputI want to handle Actor execution failures gracefully and return meaningful error messages to the LLM

Best for

AI agents that need to execute long-running web scraping or data extraction tasks

Workflows where LLM decisions trigger Actor runs and subsequent steps depend on results

Teams building chatbots that perform real-time data collection via Actors

Requires

Apify API token with Actor execution permissions

Actor must be published or in user's account

Sufficient Apify credits/quota for Actor execution

Limitations

Actor execution time is not bounded by MCP timeout — long-running Actors (>5 minutes) may cause MCP client timeouts

No built-in result streaming — entire Actor output is buffered and returned at completion, limiting real-time feedback

Cannot cancel in-flight Actor runs through MCP — cancellation requires separate Apify API calls

What makes it unique

Manages full Actor execution lifecycle through MCP protocol, handling asynchronous polling, result buffering, and timeout/error recovery without requiring the LLM client to manage execution state

vs alternatives

Abstracts Actor execution complexity compared to direct API calls, providing synchronous-style tool calling interface for asynchronous Actor runs

parameter validation and schema enforcement

Medium confidence

Validates MCP tool call parameters against Actor input schemas before execution, enforcing type constraints, required fields, and allowed values defined in the Actor's JSON schema. Implements JSON Schema validation using standard validators, rejecting invalid parameters with detailed error messages that guide the LLM to correct inputs, preventing failed Actor runs due to malformed inputs.

Solves for

I want the MCP server to reject invalid parameters before sending them to Apify, saving execution time and creditsI need clear error messages when the LLM provides incorrect parameter types or missing required fieldsI want to ensure the LLM understands parameter constraints (min/max values, allowed options, etc.)

Best for

Production systems where invalid Actor runs waste credits and time

Teams using LLMs that occasionally hallucinate parameter values

Workflows requiring high reliability and minimal failed executions

Requires

Actor input schema properly defined in Apify Actor configuration

JSON Schema validator library (typically ajv or similar)

Limitations

Validation only checks static schema constraints — cannot validate semantic correctness (e.g., valid URL format beyond regex)

Complex nested schemas with conditional requirements may not be fully validated

Error messages are technical JSON Schema validation errors — may not be user-friendly for non-technical LLM prompts

What makes it unique

Performs pre-execution JSON Schema validation against Actor input definitions, preventing invalid tool calls from reaching Apify and providing schema-aware error feedback to LLM clients

vs alternatives

Catches parameter errors before API calls compared to post-execution error handling, reducing wasted credits and improving LLM feedback loops

apify api credential management and authentication

Medium confidence

Manages Apify API authentication by accepting and securely handling API tokens, implementing credential validation, and injecting authentication headers into all Apify API requests. Supports token rotation, credential refresh, and error handling for expired/invalid tokens, ensuring the MCP server maintains authenticated access to Apify APIs without exposing credentials to MCP clients.

Solves for

I want to securely pass my Apify API token to the MCP server without exposing it to the LLM clientI need the MCP server to handle authentication failures and token expiration gracefullyI want to rotate API tokens without restarting the MCP server

Best for

Production deployments where API credentials must be protected from client exposure

Teams with credential rotation policies requiring token refresh without downtime

Multi-user systems where different users have different Apify accounts

Requires

Apify API token (from Apify account settings)

Environment variable or configuration file to pass token to MCP server

Secure transport (HTTPS/TLS) if MCP server is exposed over network

Limitations

No built-in credential encryption at rest — tokens are stored in memory only, lost on server restart

No support for OAuth or federated authentication — requires static API token

Cannot scope credentials per Actor — single token grants access to all user's Actors

What makes it unique

Centralizes Apify API authentication at the MCP server level, preventing credentials from being transmitted to or stored by MCP clients while maintaining secure API access

vs alternatives

Isolates credential handling from LLM clients compared to client-side authentication, reducing credential exposure surface area

mcp protocol compliance and message routing

Medium confidence

Implements the Model Context Protocol specification, handling JSON-RPC 2.0 message parsing, tool definition advertisement, and request/response routing between MCP clients and Apify APIs. Manages MCP lifecycle events (initialization, tool listing, tool execution), error handling with proper MCP error codes, and protocol versioning to ensure compatibility with MCP-compliant clients like Claude Desktop.

Solves for

I want my MCP server to be compatible with Claude Desktop and other standard MCP clientsI need proper MCP error handling and protocol compliance for reliable client communicationI want the server to advertise available tools correctly so clients can discover and call them

Best for

Developers building MCP servers that must work with multiple MCP clients

Teams using Claude Desktop or other MCP-compatible applications

Organizations standardizing on MCP for tool integration

Requires

@modelcontextprotocol/sdk package

MCP-compatible client (Claude Desktop, custom MCP client)

Node.js 16+

Limitations

MCP protocol version is fixed at implementation time — cannot dynamically upgrade protocol without server restart

No built-in support for MCP extensions or custom protocol features — limited to standard MCP spec

Message size limits imposed by MCP protocol may truncate large Actor outputs

What makes it unique

Implements full MCP server specification with JSON-RPC 2.0 message handling, tool advertisement, and lifecycle management, ensuring seamless integration with MCP-compliant clients

vs alternatives

Provides standards-based protocol implementation compared to custom API wrappers, enabling compatibility with any MCP client

error handling and failure recovery

Medium confidence

Implements comprehensive error handling for Apify API failures, network issues, timeouts, and invalid Actor configurations, translating errors into MCP-compatible error responses with actionable messages. Includes retry logic for transient failures, timeout management for long-running Actors, and graceful degradation when Apify APIs are unavailable, ensuring the MCP server remains stable and provides meaningful feedback to clients.

Solves for

I want the MCP server to retry failed Actor runs automatically instead of immediately failingI need clear error messages when Apify APIs are unavailable or Actor execution failsI want the server to handle network timeouts gracefully without hanging the MCP client

Best for

Production systems requiring high availability and resilience

Workflows where transient failures should be retried automatically

Teams building mission-critical AI agents that cannot tolerate frequent failures

Requires

Network connectivity to Apify API

Proper timeout configuration in MCP server settings

Limitations

Retry logic may mask underlying issues — repeated failures eventually exhaust retries without root cause analysis

Timeout values are fixed — cannot be dynamically adjusted per Actor or client

No circuit breaker pattern — server continues attempting Apify API calls even during extended outages

What makes it unique

Implements MCP-aware error handling with retry logic and timeout management, translating Apify API errors into standardized MCP error responses with recovery suggestions

vs alternatives

Provides automatic retry and timeout handling compared to client-side error management, improving reliability without requiring client-side retry logic

configuration and environment setup

Medium confidence

Manages MCP server configuration through environment variables, configuration files, or programmatic setup, including Apify API token, server port, logging level, and Actor discovery settings. Provides initialization hooks for custom configuration loading, validation of required settings, and defaults for optional parameters, enabling flexible deployment across different environments (local development, Docker, cloud platforms).

Solves for

I want to configure the MCP server with environment variables for Docker/cloud deploymentI need to set custom logging levels and debug output for troubleshootingI want to customize Actor discovery behavior (e.g., filter Actors by tag or category)

Best for

DevOps teams deploying MCP servers in containerized environments

Developers setting up local development environments with minimal configuration

Organizations with complex deployment pipelines requiring environment-specific configs

Requires

Node.js environment with access to environment variables

Configuration file (optional, format depends on implementation)

Limitations

Configuration is static at server startup — runtime configuration changes require server restart

No built-in configuration validation — invalid settings may cause silent failures

Limited configuration options — cannot customize all MCP server behaviors

What makes it unique

Provides flexible configuration management through environment variables and configuration files, supporting multiple deployment scenarios without code changes

vs alternatives

Enables environment-specific configuration compared to hardcoded settings, supporting diverse deployment contexts

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @apify/actors-mcp-server, ranked by overlap. Discovered automatically through the match graph.

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** - [Actors MCP Server](https://apify.com/apify/actors-mcp-server): Use 3,000+ pre-built cloud tools to extract data from websites, e-commerce, social media, search engines, maps, and more

mcp-compliant actor tool registration and schema transformationdynamic tool management with actor mcp tool compositionactor execution with streaming result deliveryapify platform integration with api token authentication

4 shared capabilities

MCP Server41

apify-mcp-server

The Apify MCP server enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, or any other website using thousands of ready-made scrapers, crawlers, and automation tools available on the Apify Store.

mcp-compliant tool exposure for apify actorsactor mcp tool proxying (nested mcp server support)schema transformation and validation for actor inputsapify api integration with authentication and rate limiting

4 shared capabilities

MCP Server39

@apify/actors-mcp-server

Apify MCP Server

actor discovery and schema introspectionactor execution via mcp protocolactor result streaming and paginationactor execution monitoring and status tracking

4 shared capabilities

API39

Apify

Web scraping platform with 2,000+ ready-made scrapers.

mcp (model context protocol) server integration

1 shared capability

MCP Server40

@executeautomation/playwright-mcp-server

Model Context Protocol servers for Playwright

mcp-protocol-server-lifecycle-management

1 shared capability

MCP Server39

API-mega-list

This GitHub repo is a powerhouse collection of APIs you can start using immediately to build everything from simple automations to full-scale applications. One of the most valuable API lists on GitHub—period. 💪

mcp server api discovery and integration support

1 shared capability

Best For

✓AI agent developers building automation workflows that require web scraping or data extraction
✓Teams using Claude with MCP clients who want native Apify integration
✓Developers migrating from REST API calls to standardized MCP tool calling
✓Teams with multiple Actors who want dynamic tool discovery without hardcoding schemas
✓Developers building multi-tenant systems where Actor availability varies per user
✓Organizations that frequently add or modify Actors and need schema updates without redeployment
✓AI agents that need to execute long-running web scraping or data extraction tasks
✓Workflows where LLM decisions trigger Actor runs and subsequent steps depend on results

Known Limitations

⚠Requires running a separate Node.js process to host the MCP server — adds deployment complexity vs direct API calls
⚠MCP protocol overhead adds ~50-100ms latency per tool invocation compared to direct HTTP
⚠Limited to tools exposed by Apify's Actor API — cannot wrap custom business logic not available through Actors
⚠No built-in authentication caching — each MCP request must include or refresh Apify API credentials
⚠Schema generation depends on Actor developers providing complete input/output JSON schemas — incomplete schemas result in poor LLM understanding
⚠Discovery latency scales with number of Actors in account — accounts with 100+ Actors may experience 2-5 second discovery delays

Requirements

Node.js 16+Apify API token (available from Apify account settings)@modelcontextprotocol/sdk packageMCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)Apify API token with read access to Actor metadataActors must have properly defined input/output JSON schemas in their configurationNetwork connectivity to Apify API (api.apify.com)Apify API token with Actor execution permissions

Input / Output

Accepts: MCP tool call requests (JSON-RPC 2.0 format), Actor configuration parameters (URLs, selectors, options), Apify API credentials (token), Apify API token, Actor metadata from Apify API (JSON), MCP tool call with Actor name and input parameters, Actor input JSON matching Actor's input schema, MCP tool call parameters (JSON), Actor input JSON schema, Apify API token (string), Token validation requests, MCP JSON-RPC 2.0 requests, Tool call requests, Initialization messages, Failed API requests, Timeout events, Error responses from Apify, Environment variables, Configuration files (JSON, YAML, or similar), Programmatic configuration objects

Produces: MCP tool result responses (JSON-RPC 2.0), Actor execution status and results, Structured data from Actor runs, MCP ToolDefinition objects, JSON schemas for tool parameters, Tool descriptions and usage hints, Actor execution status (queued, running, succeeded, failed), Final Actor output (JSON or structured data), Error messages and failure reasons, Validation success/failure status, Detailed validation error messages, Corrected parameter suggestions (optional), Authentication status (valid/invalid), Error messages for auth failures, MCP JSON-RPC 2.0 responses, Tool definitions, Tool results, Error responses, MCP error responses, Retry status, Failure reasons and suggestions, Validated configuration object, Configuration validation errors

UnfragileRank

Adoption49%(30% weight)

Quality17%(25% weight)

Ecosystem45%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

Visit @apify/actors-mcp-server→

Package Details

npm

Registry

0.9.19

Version

13,644

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Capabilities8 decomposed

mcp server instantiation for apify actors

Medium confidence

Solves for

Best for

AI agent developers building automation workflows that require web scraping or data extraction

Teams using Claude with MCP clients who want native Apify integration

Developers migrating from REST API calls to standardized MCP tool calling

Requires

Node.js 16+

Apify API token (available from Apify account settings)

@modelcontextprotocol/sdk package

Limitations

Requires running a separate Node.js process to host the MCP server — adds deployment complexity vs direct API calls

MCP protocol overhead adds ~50-100ms latency per tool invocation compared to direct HTTP

Limited to tools exposed by Apify's Actor API — cannot wrap custom business logic not available through Actors

What makes it unique

vs alternatives

Provides native MCP integration for Apify Actors without custom wrapper code, whereas direct HTTP calls require manual schema definition and lack MCP protocol standardization

actor discovery and tool schema generation

Medium confidence

Solves for

Best for

Teams with multiple Actors who want dynamic tool discovery without hardcoding schemas

Developers building multi-tenant systems where Actor availability varies per user

Organizations that frequently add or modify Actors and need schema updates without redeployment

Requires

Apify API token with read access to Actor metadata

Actors must have properly defined input/output JSON schemas in their configuration

Network connectivity to Apify API (api.apify.com)

Limitations

Schema generation depends on Actor developers providing complete input/output JSON schemas — incomplete schemas result in poor LLM understanding

Discovery latency scales with number of Actors in account — accounts with 100+ Actors may experience 2-5 second discovery delays

No caching of discovered schemas — each server restart triggers full re-discovery, adding startup time

What makes it unique

vs alternatives

Eliminates manual schema definition compared to generic MCP servers, automatically staying in sync with Actor configuration changes

actor execution and result streaming

Medium confidence

Solves for

Best for

AI agents that need to execute long-running web scraping or data extraction tasks

Workflows where LLM decisions trigger Actor runs and subsequent steps depend on results

Teams building chatbots that perform real-time data collection via Actors

Requires

Apify API token with Actor execution permissions

Actor must be published or in user's account

Sufficient Apify credits/quota for Actor execution

Limitations

Actor execution time is not bounded by MCP timeout — long-running Actors (>5 minutes) may cause MCP client timeouts

No built-in result streaming — entire Actor output is buffered and returned at completion, limiting real-time feedback

Cannot cancel in-flight Actor runs through MCP — cancellation requires separate Apify API calls

What makes it unique

Manages full Actor execution lifecycle through MCP protocol, handling asynchronous polling, result buffering, and timeout/error recovery without requiring the LLM client to manage execution state

vs alternatives

Abstracts Actor execution complexity compared to direct API calls, providing synchronous-style tool calling interface for asynchronous Actor runs

parameter validation and schema enforcement

Medium confidence

Solves for

Best for

Production systems where invalid Actor runs waste credits and time

Teams using LLMs that occasionally hallucinate parameter values

Workflows requiring high reliability and minimal failed executions

Requires

Actor input schema properly defined in Apify Actor configuration

JSON Schema validator library (typically ajv or similar)

Limitations

Validation only checks static schema constraints — cannot validate semantic correctness (e.g., valid URL format beyond regex)

Complex nested schemas with conditional requirements may not be fully validated

Error messages are technical JSON Schema validation errors — may not be user-friendly for non-technical LLM prompts

What makes it unique

Performs pre-execution JSON Schema validation against Actor input definitions, preventing invalid tool calls from reaching Apify and providing schema-aware error feedback to LLM clients

vs alternatives

Catches parameter errors before API calls compared to post-execution error handling, reducing wasted credits and improving LLM feedback loops

apify api credential management and authentication

Medium confidence

Solves for

Best for

Production deployments where API credentials must be protected from client exposure

Teams with credential rotation policies requiring token refresh without downtime

Multi-user systems where different users have different Apify accounts

Requires

Apify API token (from Apify account settings)

Environment variable or configuration file to pass token to MCP server

Secure transport (HTTPS/TLS) if MCP server is exposed over network

Limitations

No built-in credential encryption at rest — tokens are stored in memory only, lost on server restart

No support for OAuth or federated authentication — requires static API token

Cannot scope credentials per Actor — single token grants access to all user's Actors

What makes it unique

Centralizes Apify API authentication at the MCP server level, preventing credentials from being transmitted to or stored by MCP clients while maintaining secure API access

vs alternatives

Isolates credential handling from LLM clients compared to client-side authentication, reducing credential exposure surface area

mcp protocol compliance and message routing

Medium confidence

Solves for

Best for

Developers building MCP servers that must work with multiple MCP clients

Teams using Claude Desktop or other MCP-compatible applications

Organizations standardizing on MCP for tool integration

Requires

@modelcontextprotocol/sdk package

MCP-compatible client (Claude Desktop, custom MCP client)

Node.js 16+

Limitations

MCP protocol version is fixed at implementation time — cannot dynamically upgrade protocol without server restart

No built-in support for MCP extensions or custom protocol features — limited to standard MCP spec

Message size limits imposed by MCP protocol may truncate large Actor outputs

What makes it unique

Implements full MCP server specification with JSON-RPC 2.0 message handling, tool advertisement, and lifecycle management, ensuring seamless integration with MCP-compliant clients

vs alternatives

Provides standards-based protocol implementation compared to custom API wrappers, enabling compatibility with any MCP client

error handling and failure recovery

Medium confidence

Solves for

Best for

Production systems requiring high availability and resilience

Workflows where transient failures should be retried automatically

Teams building mission-critical AI agents that cannot tolerate frequent failures

Requires

Network connectivity to Apify API

Proper timeout configuration in MCP server settings

Limitations

Retry logic may mask underlying issues — repeated failures eventually exhaust retries without root cause analysis

Timeout values are fixed — cannot be dynamically adjusted per Actor or client

No circuit breaker pattern — server continues attempting Apify API calls even during extended outages

What makes it unique

Implements MCP-aware error handling with retry logic and timeout management, translating Apify API errors into standardized MCP error responses with recovery suggestions

vs alternatives

Provides automatic retry and timeout handling compared to client-side error management, improving reliability without requiring client-side retry logic

configuration and environment setup

Medium confidence

Solves for

Best for

DevOps teams deploying MCP servers in containerized environments

Developers setting up local development environments with minimal configuration

Organizations with complex deployment pipelines requiring environment-specific configs

Requires

Node.js environment with access to environment variables

Configuration file (optional, format depends on implementation)

Limitations

Configuration is static at server startup — runtime configuration changes require server restart

No built-in configuration validation — invalid settings may cause silent failures

Limited configuration options — cannot customize all MCP server behaviors

What makes it unique

Provides flexible configuration management through environment variables and configuration files, supporting multiple deployment scenarios without code changes

vs alternatives

Enables environment-specific configuration compared to hardcoded settings, supporting diverse deployment contexts

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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IntelliCode50Extension

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GitHub Copilot52Extension

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Claude Code for VS Code52Extension

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@apify/actors-mcp-server

Capabilities8 decomposed

mcp server instantiation for apify actors

actor discovery and tool schema generation

actor execution and result streaming

parameter validation and schema enforcement

apify api credential management and authentication

mcp protocol compliance and message routing

error handling and failure recovery

configuration and environment setup

Related Artifactssharing capabilities

Apify

apify-mcp-server

@apify/actors-mcp-server

Apify

@executeautomation/playwright-mcp-server

API-mega-list

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @apify/actors-mcp-server

Are you the builder of @apify/actors-mcp-server?

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Data Sources

@apify/actors-mcp-server

Capabilities8 decomposed

mcp server instantiation for apify actors

actor discovery and tool schema generation

actor execution and result streaming

parameter validation and schema enforcement

apify api credential management and authentication

mcp protocol compliance and message routing

error handling and failure recovery

configuration and environment setup

Related Artifactssharing capabilities

Apify

apify-mcp-server

@apify/actors-mcp-server

Apify

@executeautomation/playwright-mcp-server

API-mega-list

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @apify/actors-mcp-server

Are you the builder of @apify/actors-mcp-server?

Get the weekly brief

Data Sources