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

Hosted MCP (keyless free tier)

Connect to the remote hosted server with no setup:

https://mcp.firecrawl.dev/v2/mcp

On the keyless free tier, scrape, search, and interact work without an API key (rate-limited). Other tools such as crawl, map, agent, and extract still need a key.

Prefer an API key or OAuth whenever the human can sign up. It unlocks the full tool set and higher limits. With a key, use:

https://mcp.firecrawl.dev/{FIRECRAWL_API_KEY}/v2/mcp

See the MCP server docs and the agent onboarding guide for setup details.

Running with npx

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

Manual Installation

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

1. Open Cursor Settings
2. Go to Features > MCP Servers
3. Click "+ Add new global MCP server"
4. Enter the following code:
   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

1. Open Cursor Settings
2. Go to Features > MCP Servers
3. Click "+ Add New MCP Server"
4. 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:

{
  "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:

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:

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).

{
  "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}"
      }
    }
  }
}

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.

Configuration Examples

For cloud API usage:

export FIRECRAWL_API_KEY=your-api-key

For self-hosted instance:

# 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

Usage with Claude Desktop

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"
      }
    }
  }
}

How to Choose a Tool

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

• If you know the exact URL you want: use scrape (with JSON format for structured data)
• If you have multiple known URLs: call scrape for each URL. If you specifically need one bulk API operation, use the Firecrawl API batch endpoint outside MCP.
• 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 interact with a URL for a fresh page, or scrape + interact when you already scraped the page or need tighter scrape control

Quick Reference Table

Format Selection Guide

When using 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 repeated scrape calls for known URLs, or map + scrape to discover URLs first, or crawl for full page content)
• When you're unsure which page contains the information (use search)

Common mistakes:

• Passing a list of URLs to one scrape call. Call scrape once per URL in MCP. If you specifically need one bulk API operation, use the Firecrawl API batch endpoint outside MCP.
• 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):

{
  "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):

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

Usage Example (branding format - extract brand identity):

{
  "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. 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)
• 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:

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

Returns:

• Array of URLs found on the site

3. 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:

{
  "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."

3b. 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:

{
  "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.

3c. Generic Feedback Tool (firecrawl_feedback)

Sends structured feedback for a completed v2 endpoint job through /v2/feedback. Use this for endpoint-level feedback on scrape, parse, map, or search jobs. For search-result quality specifically, prefer firecrawl_search_feedback because it includes search-specific guidance.

Keep feedback concise: use issue codes, tags, short notes, URLs, page numbers, and small metadata objects. Do not include raw scrape/parse outputs.

Opt out: set FIRECRAWL_NO_ENDPOINT_FEEDBACK=1 (or FIRECRAWL_DISABLE_ENDPOINT_FEEDBACK=1) in the environment when starting the MCP server. The firecrawl_feedback tool will not be registered, so agents cannot call it.

Usage Example:

{
  "name": "firecrawl_feedback",
  "arguments": {
    "endpoint": "scrape",
    "jobId": "0193f6c5-1234-7890-abcd-1234567890ab",
    "rating": "partial",
    "issues": ["missing_markdown"],
    "tags": ["docs"],
    "note": "The pricing table was missing from the markdown output.",
    "url": "https://example.com/pricing",
    "pageNumbers": [1],
    "metadata": {
      "format": "markdown"
    }
  }
}

Returns:

• { success, feedbackId, creditsRefunded, creditsRefundedToday?, dailyRefundCap?, dailyCapReached?, alreadySubmitted?, warning? } JSON.

4. Crawl Tool (firecrawl_crawl)

Starts a crawl job, polls until it reaches a terminal state, and returns the final crawl status/data.

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 + scrape for tighter control)
• 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 + scrape for tighter control.

Common mistakes:

• Setting limit or maxDiscoveryDepth 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:

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

Returns:

• Final crawl status and data after internal polling, including id, status, completed, total, creditsUsed, expiresAt, next, and data. Use the returned id with firecrawl_check_crawl_status if you need to re-check the job later.

5. Check Crawl Status (firecrawl_check_crawl_status)

Check the status and results of an existing crawl job by ID.

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

Returns:

• Response includes the status of the crawl job:

6. Parse Tool (firecrawl_parse)

Parse local files or hosted upload references with Firecrawl's /v2/parse endpoint.

Best for: PDFs, Word documents, spreadsheets, HTML files, and other documents that need markdown or structured JSON output. Hosted MCP supports a two-step upload-ref flow; local direct file reads require a self-hosted FIRECRAWL_API_URL.

Not recommended for: Remote URLs (use scrape), multiple files in one call (call parse once per file), or browser-only actions such as screenshots and clicks.

Hosted MCP flow: Hosted MCP cannot read the caller's filesystem directly. Call firecrawl_parse with filePath to receive a short-lived upload command and nextToolCall, upload the file locally, then call firecrawl_parse again with the returned uploadRef. Minting the hosted upload URL requires Firecrawl auth or keyless eligibility. In local npx firecrawl-mcp mode, direct file parsing currently requires FIRECRAWL_API_URL pointing to a self-hosted Firecrawl API; a plain cloud API-key-only local server cannot read and upload files through this tool.

Usage Example:

{
  "name": "firecrawl_parse",
  "arguments": {
    "filePath": "/absolute/path/to/document.pdf",
    "formats": ["markdown"],
    "parsers": ["pdf"],
    "zeroDataRetention": true
  }
}

Returns: Parsed document content or hosted upload instructions with a nextToolCall.

7. 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:

{
  "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

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

8. 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:

1. Call firecrawl_agent with your prompt/schema → returns job ID
2. Do other work while the agent researches (can take minutes for complex queries)
3. Poll firecrawl_agent_status with the job ID to check progress
4. 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):

{
  "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):

{
  "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.

9. 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".

{
  "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

10. Interact Tool (firecrawl_interact)

Interact with a fresh URL or with a page that was already opened by firecrawl_scrape.

Best for: Clicking, typing, navigating, and extracting state from dynamic pages without restoring the deprecated browser tools.

Usage options:

• Pass url to scrape and open a page for interaction in one MCP call.
• Pass scrapeId to continue interacting with an existing scraped page.
• Pass exactly one of url or scrapeId, plus either prompt or code.

Usage Example:

{
  "name": "firecrawl_interact",
  "arguments": {
    "url": "https://example.com",
    "prompt": "Click the pricing link and summarize the visible plans"
  }
}

Returns: Interaction result and, for URL mode, the derived scrapeId for follow-up or cleanup.

11. Stop Interact Tool (firecrawl_interact_stop)

Stop an interact session for a scraped page when you are done interacting.

{
  "name": "firecrawl_interact_stop",
  "arguments": {
    "scrapeId": "scrape-id-here"
  }
}

12. Research Tools (firecrawl_research_*)

Search and inspect papers and GitHub repositories through the research MCP tools.

Available research tools:

• firecrawl_research_search_papers: search research papers.
• firecrawl_research_inspect_paper: inspect one paper.
• firecrawl_research_related_papers: find related papers.
• firecrawl_research_read_paper: read paper content.
• firecrawl_research_search_github: search GitHub repositories.

Best for: Literature review, paper lookup, and repository discovery workflows where the agent needs a focused research surface instead of general web scraping.

13. 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.

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

Multiple pages with webhooks:

{
  "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.

{
  "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_delete: delete a monitor (destructive; only call when the user intends to remove it).
• 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
• Rate limit tracking
• Error conditions

Example log messages:

[INFO] Firecrawl MCP Server initialized successfully
[INFO] Starting scrape for URL: https://example.com
[ERROR] Rate limit exceeded

Error Handling

The server provides robust error handling:

• API rate-limit errors surfaced to the MCP client
• Detailed error messages
• Network resilience

Example error response:

{
  "content": [
    {
      "type": "text",
      "text": "Error: Rate limit exceeded"
    }
  ],
  "isError": true
}

Development

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

Contributing

1. Fork the repository
2. Create your feature branch
3. Run tests: npm test
4. 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