Firecrawl MCP Server

A Model Context Protocol (MCP) server that brings Firecrawl to MCP-compatible AI agents — search, scrape, and interact with the live web for clean, agent-ready context.

Big thanks to @vrknetha, @knacklabs for the initial implementation!

Features

Search the web and get full page content
Scrape any URL into clean, structured data
Interact with pages — click, navigate, and operate
Deep research with autonomous agent
Automatic retries and rate limiting
Cloud and self-hosted support
SSE support

Play around with our MCP Server on MCP.so's playground or on Klavis AI.

Installation

Running with npx

hljs language-bash

env FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp

Manual Installation

hljs language-bash

npm install -g firecrawl-mcp

Running on Cursor

Configuring Cursor 🖥️ Note: Requires Cursor version 0.45.6+ For the most up-to-date configuration instructions, please refer to the official Cursor documentation on configuring MCP servers: Cursor MCP Server Configuration Guide

To configure Firecrawl MCP in Cursor v0.48.6

Open Cursor Settings
Go to Features > MCP Servers
Click "+ Add new global MCP server"

Enter the following code:

hljs language-json

{
  "mcpServers": {
    "firecrawl-mcp": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR-API-KEY"
      }
    }
  }
}

To configure Firecrawl MCP in Cursor v0.45.6

Open Cursor Settings
Go to Features > MCP Servers
Click "+ Add New MCP Server"
Enter the following:
- Name: "firecrawl-mcp" (or your preferred name)
- Type: "command"
- Command: env FIRECRAWL_API_KEY=your-api-key npx -y firecrawl-mcp

If you are using Windows and are running into issues, try cmd /c "set FIRECRAWL_API_KEY=your-api-key && npx -y firecrawl-mcp"

Replace your-api-key with your Firecrawl API key. If you don't have one yet, you can create an account and get it from https://www.firecrawl.dev/app/api-keys

After adding, refresh the MCP server list to see the new tools. The Composer Agent will automatically use Firecrawl MCP when appropriate, but you can explicitly request it by describing your web scraping needs. Access the Composer via Command+L (Mac), select "Agent" next to the submit button, and enter your query.

Running on Windsurf

Add this to your ./codeium/windsurf/model_config.json:

hljs language-json

{
  "mcpServers": {
    "mcp-server-firecrawl": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Running with Streamable HTTP Local Mode

To run the server using Streamable HTTP locally instead of the default stdio transport:

hljs language-bash

env HTTP_STREAMABLE_SERVER=true FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp

Use the url: http://localhost:3000/mcp

Installing via Smithery (Legacy)

To install Firecrawl for Claude Desktop automatically via Smithery:

hljs language-bash

npx -y @smithery/cli install @mendableai/mcp-server-firecrawl --client claude

Running on VS Code

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).

hljs language-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:

hljs language-json

{
  "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}"
      }
    }
  }
}

Configuration

Environment Variables

Required for Cloud API

FIRECRAWL_API_KEY: Your Firecrawl API key
- Required when using cloud API (default)
- Optional when using self-hosted instance with FIRECRAWL_API_URL
FIRECRAWL_API_URL (Optional): Custom API endpoint for self-hosted instances
- Example: https://firecrawl.your-domain.com
- If not provided, the cloud API will be used (requires API key)

MCP OAuth (Bearer access tokens)

Hosted Firecrawl can issue OAuth access tokens (fco_…) via the authorization server on firecrawl.dev. This MCP server forwards whichever credential it resolves to the Firecrawl API as Authorization: Bearer ….

HTTP stream transports (CLOUD_SERVICE=true, HTTP_STREAMABLE_SERVER=true, or SSE_LOCAL=true): Clients should send Authorization: Bearer <fco_access_token> on MCP requests. An OAuth bearer token takes precedence over x-firecrawl-api-key / x-api-key when both are present.
stdio: Use FIRECRAWL_OAUTH_TOKEN for a static access token, or keep using FIRECRAWL_API_KEY for an API key.

Use access tokens (fco_…) only. Refresh tokens (fcr_…) must be exchanged at the token endpoint, not passed to the scrape/search API.

Optional Configuration

Retry Configuration

FIRECRAWL_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)

Credit Usage Monitoring

FIRECRAWL_CREDIT_WARNING_THRESHOLD: Credit usage warning threshold (default: 1000)
FIRECRAWL_CREDIT_CRITICAL_THRESHOLD: Credit usage critical threshold (default: 100)

Configuration Examples

For cloud API usage with custom retry and credit monitoring:

hljs language-bash

# Required for cloud API
export FIRECRAWL_API_KEY=your-api-key

# Optional retry configuration
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

# Optional credit monitoring
export FIRECRAWL_CREDIT_WARNING_THRESHOLD=2000    # Warning at 2000 credits
export FIRECRAWL_CREDIT_CRITICAL_THRESHOLD=500    # Critical at 500 credits

For self-hosted instance:

hljs language-bash

# Required for self-hosted
export FIRECRAWL_API_URL=https://firecrawl.your-domain.com

# Optional authentication for self-hosted
export FIRECRAWL_API_KEY=your-api-key  # If your instance requires auth

# Custom retry configuration
export FIRECRAWL_RETRY_MAX_ATTEMPTS=10
export FIRECRAWL_RETRY_INITIAL_DELAY=500     # Start with faster retries

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

hljs language-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"
      }
    }
  }
}

System Configuration

The server includes several configurable parameters that can be set via environment variables. Here are the default values if not configured:

hljs language-typescript

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:

Retry Behavior
- Automatically retries failed requests due to rate limits
- Uses exponential backoff to avoid overwhelming the API
- Example: With default settings, retries will be attempted at:
  - 1st retry: 1 second delay
  - 2nd retry: 2 seconds delay
  - 3rd retry: 4 seconds delay (capped at maxDelay)
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

Rate Limiting and Batch Processing

The server utilizes Firecrawl's built-in rate limiting and batch processing capabilities:

Automatic rate limit handling with exponential backoff
Efficient parallel processing for batch operations
Smart request queuing and throttling
Automatic retries for transient errors

How to Choose a Tool

Use this guide to select the right tool for your task:

If you know the exact URL(s) you want:
- For one: use scrape (with JSON format for structured data)
- For many: use batch_scrape
If you need to discover URLs on a site: use map
If you want to search the web for info: use search
If you need complex research across multiple unknown sources: use agent
If you want to analyze a whole site or section: use crawl (with limits!)
If you need interactive browser automation (click, type, navigate): use scrape + interact

Quick Reference Table

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)

Format Selection Guide

When using scrape or batch_scrape, choose the right format:

JSON format (recommended for most cases): Use when you need specific data from a page. Define a schema based on what you need to extract. This keeps responses small and avoids context window overflow.
Markdown format (use sparingly): Only when you genuinely need the full page content, such as reading an entire article for summarization or analyzing page structure.

Available Tools

1. Scrape Tool (`firecrawl_scrape`)

Scrape content from a single URL with advanced options.

Best for:

Single page content extraction, when you know exactly which page contains the information.

Not recommended for:

Extracting content from multiple pages (use batch_scrape for known URLs, or map + batch_scrape to discover URLs first, or crawl for full page content)
When you're unsure which page contains the information (use search)

Common mistakes:

Using scrape for a list of URLs (use batch_scrape instead).
Using markdown format by default (use JSON format to extract only what you need).

Choosing the right format:

JSON format (preferred): For most use cases, use JSON format with a schema to extract only the specific data needed. This keeps responses focused and prevents context window overflow.
Markdown format: Only when the task genuinely requires full page content (e.g., summarizing an entire article, analyzing page structure).

Prompt Example:

"Get the product details from https://example.com/product."

Usage Example (JSON format - preferred):

hljs language-json

{
  "name": "firecrawl_scrape",
  "arguments": {
    "url": "https://example.com/product",
    "formats": [
      {
        "type": "json",
        "prompt": "Extract the product information",
        "schema": {
          "type": "object",
          "properties": {
            "name": { "type": "string" },
            "price": { "type": "number" },
            "description": { "type": "string" }
          },
          "required": ["name", "price"]
        }
      }
    ]
  }
}

Usage Example (markdown format - when full content needed):

hljs language-json

{
  "name": "firecrawl_scrape",
  "arguments": {
    "url": "https://example.com/article",
    "formats": ["markdown"],
    "onlyMainContent": true
  }
}

Usage Example (branding format - extract brand identity):

hljs language-json

{
  "name": "firecrawl_scrape",
  "arguments": {
    "url": "https://example.com",
    "formats": ["branding"]
  }
}

Branding format: Extracts comprehensive brand identity (colors, fonts, typography, spacing, logo, UI components) for design analysis or style replication. Privacy: Set redactPII: true to return content with personally identifiable information redacted.

Returns:

JSON structured data, markdown, branding profile, or other formats as specified.

2. Batch Scrape Tool (`firecrawl_batch_scrape`)

Scrape multiple URLs efficiently with built-in rate limiting and parallel processing.

Best for:

Retrieving content from multiple pages, when you know exactly which pages to scrape.

Not recommended for:

Discovering URLs (use map first if you don't know the URLs)
Scraping a single page (use scrape)

Common mistakes:

Using batch_scrape with too many URLs at once (may hit rate limits or token overflow)

Prompt Example:

"Get the content of these three blog posts: [url1, url2, url3]."

Usage Example:

hljs language-json

{
  "name": "firecrawl_batch_scrape",
  "arguments": {
    "urls": ["https://example1.com", "https://example2.com"],
    "options": {
      "formats": ["markdown"],
      "onlyMainContent": true
    }
  }
}

Returns:

Response includes operation ID for status checking:

hljs language-json

{
  "content": [
    {
      "type": "text",
      "text": "Batch operation queued with ID: batch_1. Use firecrawl_check_batch_status to check progress."
    }
  ],
  "isError": false
}

3. Check Batch Status (`firecrawl_check_batch_status`)

Check the status of a batch operation.

hljs language-json

{
  "name": "firecrawl_check_batch_status",
  "arguments": {
    "id": "batch_1"
  }
}

4. Map Tool (`firecrawl_map`)

Map a website to discover all indexed URLs on the site.

Best for:

Discovering URLs on a website before deciding what to scrape
Finding specific sections of a website

Not recommended for:

When you already know which specific URL you need (use scrape or batch_scrape)
When you need the content of the pages (use scrape after mapping)

Common mistakes:

Using crawl to discover URLs instead of map

Prompt Example:

"List all URLs on example.com."

Usage Example:

hljs language-json

{
  "name": "firecrawl_map",
  "arguments": {
    "url": "https://example.com"
  }
}

Returns:

Array of URLs found on the site

5. Search Tool (`firecrawl_search`)

Search the web and optionally extract content from search results.

Best for:

Finding specific information across multiple websites, when you don't know which website has the information.
When you need the most relevant content for a query

Not recommended for:

When you already know which website to scrape (use scrape)
When you need comprehensive coverage of a single website (use map or crawl)

Common mistakes:

Using crawl or map for open-ended questions (use search instead)

Usage Example:

hljs language-json

{
  "name": "firecrawl_search",
  "arguments": {
    "query": "latest AI research papers 2023",
    "limit": 5,
    "lang": "en",
    "country": "us",
    "scrapeOptions": {
      "formats": ["markdown"],
      "onlyMainContent": true,
      "redactPII": true
    }
  }
}

Returns:

Array of search results (with optional scraped content), plus an id field. Pass that id to firecrawl_search_feedback after you've used the results to refund 1 credit (search costs 2) and improve search quality.

Prompt Example:

"Find the latest research papers on AI published in 2023."

5b. Search Feedback Tool (`firecrawl_search_feedback`)

Sends structured feedback on a previous firecrawl_search result. The first feedback per search id refunds 1 credit and improves Firecrawl's search quality. Idempotent per search id.

Call this after every search you actually use (or that didn't help). Bad/partial feedback with missingContent is just as valuable as good feedback.

Opt out: set FIRECRAWL_NO_SEARCH_FEEDBACK=1 (or FIRECRAWL_DISABLE_SEARCH_FEEDBACK=1) in the environment when starting the MCP server. The firecrawl_search_feedback tool will not be registered, so agents can't call it. Team admins can also disable feedback server-side; in that case the tool is registered but always returns feedbackErrorCode: "TEAM_OPTED_OUT".

Most important field: missingContent. It's an array of specific pieces of content the agent expected to find but did not. One entry per missing topic — these aggregate across teams and tell us what to index next.

Daily refund cap (per team, per UTC day, default 100 credits). Once a team's creditsRefundedToday reaches dailyRefundCap, further submissions still record feedback but no longer refund credits. The response sets dailyCapReached: true. Agents should stop calling this tool for the rest of the UTC day when they see that flag.

Usage Example:

hljs language-json

{
  "name": "firecrawl_search_feedback",
  "arguments": {
    "searchId": "0193f6c5-1234-7890-abcd-1234567890ab",
    "rating": "good",
    "valuableSources": [
      {
        "url": "https://docs.firecrawl.dev/features/search",
        "reason": "Most up-to-date description of /search."
      }
    ],
    "missingContent": [
      {
        "topic": "Pricing for the search endpoint",
        "description": "No pricing tier table for /search specifically."
      },
      { "topic": "Per-team rate limits" }
    ],
    "querySuggestions": "Boost docs.firecrawl.dev for queries that mention 'firecrawl'"
  }
}

Returns:

{ success, feedbackId, creditsRefunded, alreadySubmitted? } JSON.

6. Crawl Tool (`firecrawl_crawl`)

Starts an asynchronous crawl job on a website and extract content from all pages.

Best for:

Extracting content from multiple related pages, when you need comprehensive coverage.

Not recommended for:

Extracting content from a single page (use scrape)
When token limits are a concern (use map + batch_scrape)
When you need fast results (crawling can be slow)

Warning: Crawl responses can be very large and may exceed token limits. Limit the crawl depth and number of pages, or use map + batch_scrape for better control.

Common mistakes:

Setting limit or maxDepth too high (causes token overflow)
Using crawl for a single page (use scrape instead)

Prompt Example:

"Get all blog posts from the first two levels of example.com/blog."

Usage Example:

hljs language-json

{
  "name": "firecrawl_crawl",
  "arguments": {
    "url": "https://example.com/blog/*",
    "maxDepth": 2,
    "limit": 100,
    "allowExternalLinks": false,
    "deduplicateSimilarURLs": true
  }
}

Returns:

Response includes operation ID for status checking:

hljs language-json

{
  "content": [
    {
      "type": "text",
      "text": "Started crawl for: https://example.com/* with job ID: 550e8400-e29b-41d4-a716-446655440000. Use firecrawl_check_crawl_status to check progress."
    }
  ],
  "isError": false
}

7. Check Crawl Status (`firecrawl_check_crawl_status`)

Check the status of a crawl job.

hljs language-json

{
  "name": "firecrawl_check_crawl_status",
  "arguments": {
    "id": "550e8400-e29b-41d4-a716-446655440000"
  }
}

Returns:

Response includes the status of the crawl job:

8. Extract Tool (`firecrawl_extract`)

Extract structured information from web pages using LLM capabilities. Supports both cloud AI and self-hosted LLM extraction.

Best for:

Extracting specific structured data like prices, names, details.

Not recommended for:

When you need the full content of a page (use scrape)
When you're not looking for specific structured data

Arguments:

urls: Array of URLs to extract information from
prompt: Custom prompt for the LLM extraction
systemPrompt: System prompt to guide the LLM
schema: JSON schema for structured data extraction
allowExternalLinks: Allow extraction from external links
enableWebSearch: Enable web search for additional context
includeSubdomains: Include subdomains in extraction

When using a self-hosted instance, the extraction will use your configured LLM. For cloud API, it uses Firecrawl's managed LLM service. Prompt Example:

"Extract the product name, price, and description from these product pages."

Usage Example:

hljs language-json

{
  "name": "firecrawl_extract",
  "arguments": {
    "urls": ["https://example.com/page1", "https://example.com/page2"],
    "prompt": "Extract product information including name, price, and description",
    "systemPrompt": "You are a helpful assistant that extracts product information",
    "schema": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "price": { "type": "number" },
        "description": { "type": "string" }
      },
      "required": ["name", "price"]
    },
    "allowExternalLinks": false,
    "enableWebSearch": false,
    "includeSubdomains": false
  }
}

Returns:

Extracted structured data as defined by your schema

hljs language-json

{
  "content": [
    {
      "type": "text",
      "text": {
        "name": "Example Product",
        "price": 99.99,
        "description": "This is an example product description"
      }
    }
  ],
  "isError": false
}

9. Agent Tool (`firecrawl_agent`)

Autonomous web research agent. This is a separate AI agent layer that independently browses the internet, searches for information, navigates through pages, and extracts structured data based on your query.

How it works:

The agent performs web searches, follows links, reads pages, and gathers data autonomously. This runs asynchronously - it returns a job ID immediately, and you poll firecrawl_agent_status to check when complete and retrieve results.

Async workflow:

Call firecrawl_agent with your prompt/schema → returns job ID
Do other work while the agent researches (can take minutes for complex queries)
Poll firecrawl_agent_status with the job ID to check progress
When status is "completed", the response includes the extracted data

Best for:

Complex research tasks where you don't know the exact URLs
Multi-source data gathering
Finding information scattered across the web
Tasks where you can do other work while waiting for results

Not recommended for:

Simple single-page scraping where you know the URL (use scrape with JSON format - faster and cheaper)

Arguments:

prompt: Natural language description of the data you want (required, max 10,000 characters)
urls: Optional array of URLs to focus the agent on specific pages
schema: Optional JSON schema for structured output

Prompt Example:

"Find the founders of Firecrawl and their backgrounds"

Usage Example (start agent, then poll for results):

hljs language-json

{
  "name": "firecrawl_agent",
  "arguments": {
    "prompt": "Find the top 5 AI startups founded in 2024 and their funding amounts",
    "schema": {
      "type": "object",
      "properties": {
        "startups": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "name": { "type": "string" },
              "funding": { "type": "string" },
              "founded": { "type": "string" }
            }
          }
        }
      }
    }
  }
}

Then poll with firecrawl_agent_status using the returned job ID.

Usage Example (with URLs - agent focuses on specific pages):

hljs language-json

{
  "name": "firecrawl_agent",
  "arguments": {
    "urls": ["https://docs.firecrawl.dev", "https://firecrawl.dev/pricing"],
    "prompt": "Compare the features and pricing information from these pages"
  }
}

Returns:

Job ID for status checking. Use firecrawl_agent_status to poll for results.

10. Check Agent Status (`firecrawl_agent_status`)

Check the status of an agent job and retrieve results when complete. Use this to poll for results after starting an agent.

Polling pattern: Agent research can take minutes for complex queries. Poll this endpoint periodically (e.g., every 10-30 seconds) until status is "completed" or "failed".

hljs language-json

{
  "name": "firecrawl_agent_status",
  "arguments": {
    "id": "550e8400-e29b-41d4-a716-446655440000"
  }
}

Possible statuses:

processing: Agent is still researching - check back later
completed: Research finished - response includes the extracted data
failed: An error occurred

11. Monitor Tools (`firecrawl_monitor_*`)

Create and manage recurring page monitors. Monitors run scheduled scrapes or crawls, diff each result against the last retained snapshot, and can notify by webhook or email.

Best for:

Watching one page or a few pages over time
Alerting on meaningful changes using a plain-English goal
Tracking check history and page-level diffs

Recommended create pattern:

Use page or pages plus goal. The MCP server builds the monitor request with a 30-minute schedule and the API enables meaningful-change judging automatically.

Meaningful-change judging runs automatically when goal is set. Page webhooks expose isMeaningful and judgment on monitor.page events.

Write goals as concise 2-3 sentence monitor instructions. Say what should trigger an alert, preserve any scope the user gave, and include intent-specific exclusions only when obvious from the request. Generic noise such as whitespace, formatting-only changes, request IDs, tracking params, generic metadata, and unrelated page chrome is already handled by the judge, so do not repeat it in every goal. If the user is vague, keep the goal broad; if they ask for broad monitoring or "any change", preserve that. If the user says they do not care about something, include that explicitly.

hljs language-json

{
  "name": "firecrawl_monitor_create",
  "arguments": {
    "page": "https://example.com/pricing",
    "goal": "Alert when pricing, packaging, or launch messaging changes."
  }
}

Multiple pages with webhooks:

hljs language-json

{
  "name": "firecrawl_monitor_create",
  "arguments": {
    "pages": ["https://example.com/pricing", "https://example.com/changelog"],
    "goal": "Alert when pricing, packaging, or launch messaging changes.",
    "webhookUrl": "https://example.com/webhooks/firecrawl"
  }
}

Advanced create requests:

Pass body when you need crawl targets, JSON change tracking, custom retention, or explicit judgeEnabled control.

hljs language-json

{
  "name": "firecrawl_monitor_create",
  "arguments": {
    "body": {
      "name": "Docs monitor",
      "schedule": { "text": "hourly", "timezone": "UTC" },
      "goal": "Alert when docs pages add, remove, or materially change API behavior.",
      "targets": [{ "type": "crawl", "url": "https://example.com/docs" }]
    }
  }
}

Other monitor tools:

firecrawl_monitor_list: list monitors.
firecrawl_monitor_get: get one monitor.
firecrawl_monitor_update: update fields including goal, judgeEnabled, webhook, and notification.
firecrawl_monitor_run: trigger a check now.
firecrawl_monitor_checks: list checks, optionally filtered by status.
firecrawl_monitor_check: get page-level results, including diff, snapshot, judgment.meaningful, and judgment.meaningfulChanges.

Logging System

The server includes comprehensive logging:

Operation status and progress
Performance metrics
Credit usage monitoring
Rate limit tracking
Error conditions

Example log messages:

hljs language-csharp

[INFO] Firecrawl MCP Server initialized successfully
[INFO] Starting scrape for URL: https://example.com
[INFO] Batch operation queued with ID: batch_1
[WARNING] Credit usage has reached warning threshold
[ERROR] Rate limit exceeded, retrying in 2s...

Error Handling

The server provides robust error handling:

Automatic retries for transient errors
Rate limit handling with backoff
Detailed error messages
Credit usage warnings
Network resilience

Example error response:

hljs language-json

{
  "content": [
    {
      "type": "text",
      "text": "Error: Rate limit exceeded. Retrying in 2 seconds..."
    }
  ],
  "isError": true
}

Development

hljs language-bash

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

Contributing

Fork the repository
Create your feature branch
Run tests: npm test
Submit a pull request

Thanks to contributors

Thanks to @vrknetha, @cawstudios for the initial implementation!

Thanks to MCP.so and Klavis AI for hosting and @gstarwd, @xiangkaiz and @zihaolin96 for integrating our server.

License

MIT License - see LICENSE file for details

Firecrawl MCP Server

Firecrawl MCP Server

Features

Installation

Running with npx

Manual Installation

Running on Cursor

Running on Windsurf

Running with Streamable HTTP Local Mode

Installing via Smithery (Legacy)

Running on VS Code

Configuration

Environment Variables

Required for Cloud API

MCP OAuth (Bearer access tokens)

Optional Configuration

Retry Configuration

Credit Usage Monitoring

Configuration Examples

Usage with Claude Desktop

System Configuration

Rate Limiting and Batch Processing

How to Choose a Tool

Quick Reference Table

Format Selection Guide

Available Tools

1. Scrape Tool (firecrawl_scrape)

2. Batch Scrape Tool (firecrawl_batch_scrape)

3. Check Batch Status (firecrawl_check_batch_status)

4. Map Tool (firecrawl_map)

5. Search Tool (firecrawl_search)

5b. Search Feedback Tool (firecrawl_search_feedback)

6. Crawl Tool (firecrawl_crawl)

7. Check Crawl Status (firecrawl_check_crawl_status)

8. Extract Tool (firecrawl_extract)

9. Agent Tool (firecrawl_agent)

10. Check Agent Status (firecrawl_agent_status)

11. Monitor Tools (firecrawl_monitor_*)

Logging System

Error Handling

Development

Contributing

Thanks to contributors

License

Similar Packages

Repository

Tags

Original Author

Publisher

In Collections

Repository

1. Scrape Tool (`firecrawl_scrape`)

2. Batch Scrape Tool (`firecrawl_batch_scrape`)

3. Check Batch Status (`firecrawl_check_batch_status`)

4. Map Tool (`firecrawl_map`)

5. Search Tool (`firecrawl_search`)

5b. Search Feedback Tool (`firecrawl_search_feedback`)

6. Crawl Tool (`firecrawl_crawl`)

7. Check Crawl Status (`firecrawl_check_crawl_status`)

8. Extract Tool (`firecrawl_extract`)

9. Agent Tool (`firecrawl_agent`)

10. Check Agent Status (`firecrawl_agent_status`)

11. Monitor Tools (`firecrawl_monitor_*`)