Firecrawl MCP vs Apify MCP Server

When to pick Firecrawl MCP

• You have a product page or documentation URL and need its content. Use firecrawl_scrape for Markdown or JSON matching your extraction schema, choosing specific fields when the full page would add unnecessary context.
• You need to collect a documentation section or discover which pages to read. firecrawl_map lists URLs without fetching their content; firecrawl_crawl collects pages with controls such as limit, includePaths, excludePaths and maxDiscoveryDepth.
• Your research starts with a query or spans several websites. Use firecrawl_search to find pages, then scrape selected results. For a dataset assembled across sources, firecrawl_agent accepts a prompt and optional schema and returns a job ID for polling.
• A page requires clicking, typing or navigation before its content becomes accessible. firecrawl_interact runs a browser turn and can continue a session through scrapeId. Each call completes its turn before returning control to the client.

When to pick Apify MCP Server

• You need a scraper designed for a particular source, such as Instagram profiles, Facebook posts or Google Maps contact details. Search with search-actors, inspect the selected Actor with fetch-actor-details, then execute it with call-actor.
• You already work with Apify datasets and need to retrieve results in parts. Actor calls return run metadata and storage IDs; get-dataset-items retrieves the records with filtering and pagination.
• Your workflow includes saved scraping configurations or scheduled runs. Optional task and schedule tools let the client create reusable Actor tasks and manage when they run. Select those categories explicitly when configuring the server.
• You want hosted access to rental Actors or inferred output schemas for structured results. Apify documents these as reasons to use its hosted endpoint; local stdio does not offer the same feature coverage.

Main differences

• Firecrawl exposes operations such as scrape, map, crawl, parse and browser interaction. Apify centers execution on Actors with their own input contracts. Its discovery tools help the model inspect those contracts before starting a run.
• Both offer local stdio and hosted connections. Firecrawl also documents local Streamable HTTP and SSE support. Apify's hosted endpoint uses Streamable HTTP; its former /sse endpoint has been removed.
• Firecrawl supports a self-hosted API through FIRECRAWL_API_URL; otherwise, requests use its cloud API. Apify sends requests and Actor inputs to the Apify API for execution, including when the MCP process runs locally.
• Both support API credentials and OAuth. Firecrawl also offers rate-limited, keyless hosted scraping, search and parsing. Apify allows unauthenticated access when the configured tool list contains only permitted discovery and documentation tools; this does not grant ordinary Actor execution.
• Firecrawl's crawl tool polls internally and returns final status and data, while its research agent uses separate status calls. Apify Actor calls return metadata and a next step; retrieving dataset records requires get-dataset-items.
• Firecrawl accounts for usage in credits; adding scrapeOptions to search bills each fetched result as a scrape. Apify costs vary by Actor, and fetch-actor-details includes pricing information alongside its schemas and documentation.

Setup

For Firecrawl, configure the client's server command as npx -y firecrawl-mcp and set FIRECRAWL_API_KEY in its environment. A hosted API-key connection uses https://mcp.firecrawl.dev/v2/mcp with an Authorization: Bearer <FIRECRAWL_API_KEY> header. Use https://mcp.firecrawl.dev/v2/mcp-oauth for the client's OAuth account flow.

For Apify, connect to https://mcp.apify.com using OAuth or an Authorization: Bearer <APIFY_TOKEN> header. For local stdio, use npx -y @apify/actors-mcp-server with APIFY_TOKEN in the environment. Set --tools locally or the hosted tools query parameter to explicitly select the tools your integration needs.