# Firecrawl MCP (`red.cars/firecrawl-mcp`) Actor AI agents that need web data without anti-bot headaches. 20 tools for API-based web scraping, crawl, search, and extract — no proxy rotation, no stealth needed. - **URL**: https://apify.com/red.cars/firecrawl-mcp.md - **Developed by:** [AutomateLab](https://apify.com/red.cars) (community) - **Categories:** MCP servers, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Firecrawl MCP API-based web scraping MCP server for AI agents. ### What is this? 20 tools for web scraping, crawling, search, and data extraction — powered by Firecrawl's API. No proxy rotation. No anti-bot complexity. Just API calls. ### Positioning **For AI agents that need web data without anti-bot headaches.** Unlike traditional scraping tools that require proxy rotation, browser stealth, and constant maintenance against anti-bot detection, Firecrawl MCP works through a simple API. The heavy lifting happens server-side. ### Tools #### Scraping - `scrape_and-extract-from-url` — Scrape a single URL and return structured data - `batch_scrape-and-extract-from-urls` — Batch scrape multiple URLs #### Crawling - `crawl_urls` — Crawl a website with configurable depth and scope - `crawl_get-status` — Get crawl status - `crawl_cancel` — Cancel a crawl job - `crawl_errors_get-crawl` — Get crawl errors - `crawl_get-active` — Get all active crawls #### Search - `search` — Search for URLs matching a query - `firecrawl-search_search-and-scrape` — Search and scrape combined #### Extraction - `extract_data` — Extract structured data from URLs using selectors - `extract_get-status` — Get extract job status #### Deep Research - `deep-research_start` — Start deep research job - `deep-research_get-status` — Get deep research status #### Maps - `map_urls` — Generate a URL map for a website #### LLM TXT - `llmstxt_generate-llms-txt` — Generate llms.txt for a site - `llmstxt_get-llms-txt-status` — Check llms.txt generation status #### Team - `team_get-credit-usage` — Get team credit usage - `team_get-token-usage` — Get team token usage #### Utilities - `context` — Get API domain context - `sync` — Sync operation - `export` — Export data - `import` — Import data - `sql` — SQL query - `workflow_status` — Get workflow status - `workflow_archive` — Archive workflow ### Installation #### Apify ```bash apify push firecrawl-mcp ```` #### Local development ```bash npm install npm run build ``` #### Environment variables | Variable | Description | |----------|-------------| | `FIRECRAWL_BEARER_AUTH` | Firecrawl API bearer token | ### Usage #### Standalone (batch input) ```json { "tool": "scrape_and-extract-from-url", "params": { "url": "https://example.com" } } ``` #### MCP Protocol (Standby mode) Send JSON-RPC 2.0 requests to `/mcp`: ```bash ## Initialize curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' ## List tools curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}' ## Call tool curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"scrape_and-extract-from-url","arguments":{"url":"https://example.com"}}}' ``` ### Architecture ``` Apify Actor (handleRequest) | v MCPProxy (Node.js child_process) | v firecrawl-pp-mcp binary (stdio) | v Firecrawl API ``` - Actor spawns `firecrawl-pp-mcp` as a subprocess with stdio transport - JSON-RPC requests are proxied through stdin/stdout - PPE charges applied via `Actor.charge()` before tool calls - Standby HTTP server handles MCP protocol over HTTP ### PPE Pricing | Tool | Price (USD) | |------|-------------| | scrape\_and-extract-from-url | $0.10 | | batch\_scrape-and-extract-from-urls | $0.10 | | crawl\_urls | $0.15 | | map\_urls | $0.05 | | search | $0.08 | | extract\_data | $0.10 | | deep-research\_start | $0.12 | | firecrawl-search\_search-and-scrape | $0.08 | | context | $0.01 | | sync | $0.02 | | export | $0.03 | | import | $0.03 | | sql | $0.05 | | workflow\_status | $0.02 | | workflow\_archive | $0.03 | ### Authentication Firecrawl uses bearer token authentication. Set `FIRECRAWL_BEARER_AUTH` in Apify secrets. ### Key Differentiators 1. **No anti-bot** — API-based scraping means no proxy rotation, no browser fingerprinting, no CAPTCHAs 2. **No API key required** — Bearer token auth via environment variable 3. **20 tools** — Covering scrape, crawl, search, extract, and utilities 4. **PPE ready** — Per-tool pricing via Apify PAY\_PER\_EVENT model ### GitHub Topics `firecrawl` `web-scraping` `ai-agents` `no-api-key-required` `mcp` `apify` ### Related - [Firecrawl Documentation](https://docs.firecrawl.dev) - [Apify MCP Documentation](https://docs.apify.com/mcp) - [Printing Press Library](https://github.com/mvanhorn/printing-press-library) ### License MIT # Actor input Schema ## `tool` (type: `string`): The firecrawl-pp-mcp tool to call (e.g., scrape\_and-extract-from-url) ## `params` (type: `object`): Arguments to pass to the tool ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("red.cars/firecrawl-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("red.cars/firecrawl-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call red.cars/firecrawl-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=red.cars/firecrawl-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Firecrawl MCP", "description": "AI agents that need web data without anti-bot headaches. 20 tools for API-based web scraping, crawl, search, and extract — no proxy rotation, no stealth needed.", "version": "1.0", "x-build-id": "5NwDVQuF2qdWM4S78" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/red.cars~firecrawl-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-red.cars-firecrawl-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/red.cars~firecrawl-mcp/runs": { "post": { "operationId": "runs-sync-red.cars-firecrawl-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/red.cars~firecrawl-mcp/run-sync": { "post": { "operationId": "run-sync-red.cars-firecrawl-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "tool" ], "properties": { "tool": { "title": "Tool name", "type": "string", "description": "The firecrawl-pp-mcp tool to call (e.g., scrape_and-extract-from-url)" }, "params": { "title": "Tool parameters", "type": "object", "description": "Arguments to pass to the tool", "additionalProperties": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```