经 AI Skill Hub 精选评估,Firecrawl MCP服务器 获评「强烈推荐」。已获得 6.3k 颗 GitHub Star,这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 8.5 分,适合有一定技术背景的用户使用。
官方开源MCP工具,将强大的网页爬取和搜索能力集成到Claude中。支持内容提取、数据采集、批量处理等功能,适合需要自动化网页数据采集的开发者和数据分析人员。
Firecrawl MCP服务器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
官方开源MCP工具,将强大的网页爬取和搜索能力集成到Claude中。支持内容提取、数据采集、批量处理等功能,适合需要自动化网页数据采集的开发者和数据分析人员。
Firecrawl MCP服务器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/firecrawl/firecrawl-mcp-server
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"firecrawl-mcp---": {
"command": "npx",
"args": ["-y", "firecrawl-mcp-server"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 Firecrawl MCP服务器 执行以下任务... Claude: [自动调用 Firecrawl MCP服务器 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"firecrawl_mcp___": {
"command": "npx",
"args": ["-y", "firecrawl-mcp-server"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Play around with our MCP Server on MCP.so's playground or on Klavis AI.
export FIRECRAWL_API_KEY=your-api-key
export FIRECRAWL_API_URL=https://firecrawl.your-domain.com
npm install
npm install -g firecrawl-mcp
To install Firecrawl for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @mendableai/mcp-server-firecrawl --client claude
npm run build
For cloud API usage with custom retry and credit monitoring:
```bash
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"mcp-server-firecrawl": {
"command": "npx",
"args": ["-y", "firecrawl-mcp"],
"env": {
"FIRECRAWL_API_KEY": "YOUR_API_KEY_HERE",
"FIRECRAWL_RETRY_MAX_ATTEMPTS": "5",
"FIRECRAWL_RETRY_INITIAL_DELAY": "2000",
"FIRECRAWL_RETRY_MAX_DELAY": "30000",
"FIRECRAWL_RETRY_BACKOFF_FACTOR": "3",
"FIRECRAWL_CREDIT_WARNING_THRESHOLD": "2000",
"FIRECRAWL_CREDIT_CRITICAL_THRESHOLD": "500"
}
}
}
}
When using scrape or batch_scrape, choose the right format:
FIRECRAWL_API_KEY: Your Firecrawl API keyFIRECRAWL_API_URLFIRECRAWL_API_URL (Optional): Custom API endpoint for self-hosted instanceshttps://firecrawl.your-domain.comFIRECRAWL_RETRY_MAX_ATTEMPTS: Maximum number of retry attempts (default: 3)FIRECRAWL_RETRY_INITIAL_DELAY: Initial delay in milliseconds before first retry (default: 1000)FIRECRAWL_RETRY_MAX_DELAY: Maximum delay in milliseconds between retries (default: 10000)FIRECRAWL_RETRY_BACKOFF_FACTOR: Exponential backoff multiplier (default: 2)FIRECRAWL_CREDIT_WARNING_THRESHOLD: Credit usage warning threshold (default: 1000)FIRECRAWL_CREDIT_CRITICAL_THRESHOLD: Credit usage critical threshold (default: 100)export FIRECRAWL_RETRY_MAX_ATTEMPTS=5 # Increase max retry attempts export FIRECRAWL_RETRY_INITIAL_DELAY=2000 # Start with 2s delay export FIRECRAWL_RETRY_MAX_DELAY=30000 # Maximum 30s delay export FIRECRAWL_RETRY_BACKOFF_FACTOR=3 # More aggressive backoff
export FIRECRAWL_CREDIT_WARNING_THRESHOLD=2000 # Warning at 2000 credits export FIRECRAWL_CREDIT_CRITICAL_THRESHOLD=500 # Critical at 500 credits
For self-hosted instance:
bash
export FIRECRAWL_API_KEY=your-api-key # If your instance requires auth
export FIRECRAWL_RETRY_MAX_ATTEMPTS=10 export FIRECRAWL_RETRY_INITIAL_DELAY=500 # Start with faster retries ```
The server includes several configurable parameters that can be set via environment variables. Here are the default values if not configured:
const CONFIG = {
retry: {
maxAttempts: 3, // Number of retry attempts for rate-limited requests
initialDelay: 1000, // Initial delay before first retry (in milliseconds)
maxDelay: 10000, // Maximum delay between retries (in milliseconds)
backoffFactor: 2, // Multiplier for exponential backoff
},
credit: {
warningThreshold: 1000, // Warn when credit usage reaches this level
criticalThreshold: 100, // Critical alert when credit usage reaches this level
},
};
These configurations control:
2. Credit Usage Monitoring - Tracks API credit consumption for cloud API usage - Provides warnings at specified thresholds - Helps prevent unexpected service interruption - Example: With default settings: - Warning at 1000 credits remaining - Critical alert at 100 credits remaining
| Tool | Best for | Returns |
|---|---|---|
| scrape | Single page content | JSON (preferred) or markdown |
| interact | Interact with a scraped page | Execution result |
| batch_scrape | Multiple known URLs | JSON (preferred) or markdown[] |
| map | Discovering URLs on a site | URL[] |
| crawl | Multi-page extraction (with limits) | markdown/html[] |
| search | Web search for info | results[] |
| agent | Complex multi-source research | JSON (structured data) |
| browser | Interactive multi-step automation (deprecated) | Session with live browser |
For one-click installation, click one of the install buttons below...
For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "apiKey",
"description": "Firecrawl API Key",
"password": true
}
],
"servers": {
"firecrawl": {
"command": "npx",
"args": ["-y", "firecrawl-mcp"],
"env": {
"FIRECRAWL_API_KEY": "${input:apiKey}"
}
}
}
}
}
Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others:
{
"inputs": [
{
"type": "promptString",
"id": "apiKey",
"description": "Firecrawl API Key",
"password": true
}
],
"servers": {
"firecrawl": {
"command": "npx",
"args": ["-y", "firecrawl-mcp"],
"env": {
"FIRECRAWL_API_KEY": "${input:apiKey}"
}
}
}
}
这是一个简介 FireCrawl MCP 服务的 README 文件。它提供了项目概述、功能特点和使用指南。
FireCrawl MCP 服务提供了多种功能,包括网络搜索、页面内容提取、页面交互、深度研究和云端浏览器会话等。它还支持自动重试和速率限制。
FireCrawl MCP 服务需要以下环境依赖:环境变量 FIRECRAWL_API_KEY 和 FIRECRAWL_API_URL,以及 npm 安装的依赖项。
FireCrawl MCP 服务可以通过以下方式安装:手动安装、使用 Smithery (Legacy) 或通过 Docker 等方式。
FireCrawl MCP 服务的使用教程包括配置示例、使用指南和 API 快速参考表等。
FireCrawl MCP 服务的配置说明包括环境变量、MCP 服务器配置和关键参数等。
FireCrawl MCP 服务的 API 说明包括 scrape、interact 和 batch_scrape 等接口的使用方法和返回值等。
官方MCP实现,集成度高。网页爬取能力强大,支持Claude直接调用。代码维护活跃,6.3k星标认可度好。适合构建数据驱动的AI应用。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:Firecrawl MCP服务器 的核心功能完整,质量优秀。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | firecrawl-mcp-server |
| 原始描述 | 开源MCP工具:🔥 Official Firecrawl MCP Server - Adds powerful web scraping and search to Curs。⭐6.3k · JavaScript |
| Topics | 网页爬虫数据采集Claude集成内容提取批量处理 |
| GitHub | https://github.com/firecrawl/firecrawl-mcp-server |
| License | MIT |
| 语言 | JavaScript |
收录时间:2026-05-14 · 更新时间:2026-05-16 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端