mcp/firecrawl

Firecrawl MCP Server

🔥 Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.

https://www.anthropic.com/news/model-context-protocol

MCP Info

Image Building Info

Available Tools (26)

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Tools Details

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. You describe what you need, and the agent figures out where to find it.

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.

IMPORTANT - Async workflow with patient polling:

1. Call firecrawl_agent with your prompt/schema → returns job ID immediately
2. Poll firecrawl_agent_status with the job ID to check progress
3. Keep polling for at least 2-3 minutes - agent research typically takes 1-5 minutes for complex queries
4. Poll every 15-30 seconds until status is "completed" or "failed"
5. Do NOT give up after just a few polling attempts - the agent needs time to research

Expected wait times:

• Simple queries with provided URLs: 30 seconds - 1 minute
• Complex research across multiple sites: 2-5 minutes
• Deep research tasks: 5+ minutes

Best for: Complex research tasks where you don't know the exact URLs; multi-source data gathering; finding information scattered across the web; extracting data from JavaScript-heavy SPAs that fail with regular scrape. Not recommended for:

• Single-page extraction when you have a URL (use firecrawl_scrape, faster and cheaper)
• Web search (use firecrawl_search first)
• Interactive page tasks like clicking, filling forms, login, or navigating JS-heavy SPAs (use firecrawl_scrape + firecrawl_interact)
• Extracting specific data from a known page (use firecrawl_scrape with JSON format)

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 patiently for results):

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 every 15-30 seconds for at least 2-3 minutes.

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

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.

This tool interacts with external entities.

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Tool: 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 with firecrawl_agent.

IMPORTANT - Be patient with polling:

• Poll every 15-30 seconds
• Keep polling for at least 2-3 minutes before ***ing the request failed
• Complex research can take 5+ minutes - do not give up early
• Only stop polling when status is "completed" or "failed"

Usage Example:

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

Possible statuses:

• processing: Agent is still researching - keep polling, do not give up
• completed: Research finished - response includes the extracted data
• failed: An error occurred (only stop polling on this status)

Returns: Status, progress, and results (if completed) of the agent job.

This tool is read-only. It does not modify its environment.

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Tool: firecrawl_check_crawl_status

Check the status of a crawl job.

Usage Example:

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

Returns: Status and progress of the crawl job, including results if available.

This tool is read-only. It does not modify its environment.

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Tool: firecrawl_crawl

Starts a crawl job on a website, 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) or too low (causes missing pages); using crawl for a single page (use scrape instead). Using a /* wildcard is not recommended. Prompt Example: "Get all blog posts from the first two levels of example.com/blog." Usage Example:

json
{
  "name": "firecrawl_crawl",
  "arguments": {
    "url": "https://example.com/blog/*",
    "maxDiscoveryDepth": 5,
    "limit": 20,
    "allowExternalLinks": false,
    "deduplicateSimilarURLs": true,
    "sitemap": "include"
  }
}

Returns: Final crawl status and data after internal polling, including the crawl id. Use firecrawl_check_crawl_status only when you need to re-check an existing crawl ID later.

This tool interacts with external entities.

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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 from web pages. 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
• 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 Prompt Example: "Extract the product name, price, and description from these product pages." Usage Example:

json
{
  "name": "firecrawl_extract",
  "arguments": {
    "urls": ["https://example.com/page1", "https://example.com/page2"],
    "prompt": "Extract product information including name, price, and description",
    "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.

This tool is read-only. It does not modify its environment.

This tool interacts with external entities.

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Tool: firecrawl_feedback

Send structured feedback for a completed Firecrawl v2 job. Use this for endpoint-level feedback on scrape, parse, map, or search jobs when the job result was useful, partially useful, or failed to meet expectations.

For search-result quality specifically, prefer firecrawl_search_feedback when available because it has search-focused guidance. This generic tool posts to /v2/feedback and accepts endpoint-wide signals:

• endpoint — one of search, scrape, parse, or map.
• jobId — the id returned by that endpoint.
• rating — overall result quality: good, partial, or bad.
• issues — stable lowercase issue codes such as missing_markdown, bad_pdf_parse, or wrong_links.
• tags — optional lowercase tags for grouping feedback.
• note — short human-readable context. Do not include huge page contents or raw scrape results.
• url, pageNumbers, and metadata — small contextual fields that identify what the feedback refers to.

Do not store multi-MB outputs in feedback. Use concise notes, issue codes, URLs, and page numbers.

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

This tool interacts with external entities.

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Tool: firecrawl_interact

Interact with a page in a live browser session: click buttons, fill forms, extract dynamic content, or navigate deeper.

Best for: Multi-step workflows on a single page — searching a site, clicking through results, filling forms, extracting data that requires interaction. Two ways to target a page:

• Pass a url to interact directly. The session is opened for you in one call (use this for a fresh page).
• Pass a scrapeId from a previous firecrawl_scrape to reuse that already-loaded page (cheaper when you just scraped it).

Arguments:

• url: Page to interact with; opens a session for you (use this OR scrapeId)
• scrapeId: Scrape job ID from a previous scrape, found in its metadata (use this OR url)
• prompt: Natural language instruction describing the action to take (use this OR code)
• code: Code to execute in the browser session (use this OR prompt)
• language: "bash", "python", or "node" (optional, defaults to "node", only used with code)
• timeout: Interact execution timeout in seconds, 1-300 (optional, defaults to 30)
• scrapeOptions: Optional scrape controls used only with url mode, such as waitFor, maxAge, proxy, or zeroDataRetention

Usage Example (prompt, direct via url):

json
{
  "name": "firecrawl_interact",
  "arguments": {
    "url": "https://example.com/products",
    "prompt": "Click on the first product and tell me its price"
  }
}

Usage Example (code):

json
{
  "name": "firecrawl_interact",
  "arguments": {
    "scrapeId": "scrape-id-from-previous-scrape",
    "code": "agent-browser click @e5",
    "language": "bash"
  }
}

Returns: Execution result including output, stdout, stderr, exit code, and live view URLs.

This tool interacts with external entities.

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Tool: firecrawl_interact_stop

Stop an interact session for a scraped page. Call this when you are done interacting to free resources.

Usage Example:

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

Returns: Success confirmation.

This tool may perform destructive updates.

---

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 or pages within a large site; locating the correct page when scrape returns empty or incomplete results. 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; jumping straight to firecrawl_agent when scrape fails instead of using map first to find the right page.

IMPORTANT - Use map before agent: If firecrawl_scrape returns empty, minimal, or irrelevant content, use firecrawl_map with the search parameter to find the specific page URL containing your target content. This is faster and cheaper than using firecrawl_agent. Only use the agent as a last resort after map+scrape fails.

Prompt Example: "Find the webhook documentation page on this API docs site." Usage Example (discover all URLs):

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

Usage Example (search for specific content - RECOMMENDED when scrape fails):

json
{
  "name": "firecrawl_map",
  "arguments": {
    "url": "https://docs.example.com/api",
    "search": "webhook events"
  }
}

Returns: Array of URLs found on the site, filtered by search query if provided.

This tool is read-only. It does not modify its environment.

This tool interacts with external entities.

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Tool: firecrawl_monitor_check

Get a single check with page-level diff results. Filter pageStatus to surface only the pages that changed (or were new, removed, etc.).

Each entry in data.pages[] has url, status (same | new | changed | removed | error), optional judgment when goal-based judging ran, and — when changed — a diff and possibly a snapshot. The shape of diff depends on the monitor's formats configuration:

• Markdown mode (default). diff.text is the unified markdown diff; diff.json is a parse-diff AST ({ files: [...] }). No snapshot.
• JSON mode (changeTracking with modes: ["json"]). diff.json is a per-field map keyed by JSON path into the extraction, e.g. plans[0].price, with each value being { previous, current }. snapshot.json is the full current extraction. No diff.text.
• Mixed mode (modes: ["json", "git-diff"]). Both diff.text (markdown sidecar) AND diff.json (per-field map) are present, plus snapshot.json.

Example JSON-mode response pages[] entry:

json
{
  "url": "https://example.com/pricing",
  "status": "changed",
  "diff": {
    "json": {
      "plans[0].price":       { "previous": "$19/mo",        "current": "$24/mo" },
      "plans[1].features[2]": { "previous": "10 GB storage", "current": "25 GB storage" }
    }
  },
  "snapshot": { "json": { "plans": [/* current full extraction matching the monitor's schema */] } },
  "judgment": {
    "meaningful": true,
    "confidence": "high",
    "reason": "The pricing changed, which matches the monitor goal.",
    "meaningfulChanges": [
      {
        "type": "changed",
        "before": "$19/mo",
        "after": "$24/mo",
        "reason": "The tracked plan price changed."
      }
    ]
  }
}

When summarizing a check for the user, prefer diff.json paths (e.g. "plans[0].price changed from $19/mo to $24/mo") over re-printing the markdown diff — it's more concise and grounded in the schema fields they asked for.

When judgment is present, use it to decide what to surface. judgment.meaningful: false means the change was classified as noise for the monitor's goal. When judgment.meaningfulChanges is present, prefer those goal-relevant changes over raw diff hunks; each item includes type, before, after, and reason.

The endpoint paginates via a top-level next URL; this tool returns one page at a time. Increase limit (max 100) to fetch fewer pages.

Usage Example:

json
{
  "name": "firecrawl_monitor_check",
  "arguments": {
    "id": "mon_abc123",
    "checkId": "chk_xyz",
    "pageStatus": "changed"
  }
}

This tool is read-only. It does not modify its environment.

---

Tool: firecrawl_monitor_checks

List historical checks for a monitor.

Usage Example:

json
{ "name": "firecrawl_monitor_checks", "arguments": { "id": "mon_abc123", "limit": 10, "status": "completed" } }

This tool is read-only. It does not modify its environment.

---

Tool: firecrawl_monitor_create

Create a Firecrawl monitor — a recurring scrape, crawl, or search that diffs each result against the last retained snapshot.

Prefer the simple path: pass page or pages plus goal to monitor specific URLs, OR pass queries plus goal to monitor web search results for new/changed hits. The tool will create the monitor with a 30-minute schedule and meaningful-change judging enabled by the API. Use body only for advanced requests such as crawl targets, JSON change tracking, custom retention, or manual judgeEnabled control.

Meaningful-change judge: set goal to a plain-language description of what the user actually cares about. judgeEnabled defaults to true when goal is set, so providing goal is enough. Page webhooks expose isMeaningful and judgment on monitor.page events.

Simple fields:

• page: one page URL to monitor.
• pages: multiple page URLs to monitor.
• queries: one or more search queries (1-12) to monitor instead of fixed URLs. Each check runs the searches and diffs the result set, so you get alerted when new or changed results appear. Mutually exclusive with page/pages in the simple path.
• searchWindow: optional recency window for search targets — one of 5m, 15m, 1h, 6h, 24h, 7d (default 24h).
• maxResults: optional max results per search, 1-50 (default 10).
• includeDomains / excludeDomains: optional domain allow/deny lists for search targets.
• goal: plain-English instruction for what changes matter. Required for the simple path (and always required when queries are set — web monitors must have a goal).
• scheduleText: optional natural-language schedule, default every 30 minutes.
• email: optional email recipient for summaries.
• webhookUrl: optional webhook URL. Configures monitor.page and monitor.check.completed.

Search-mode example:

json
{
  "name": "firecrawl_monitor_create",
  "arguments": {
    "queries": ["new LLM release", "frontier model launch"],
    "goal": "Notify me about major new LLM model releases.",
    "searchWindow": "24h",
    "maxResults": 10
  }
}

Goal guidance:

• Expand the user's one-line monitoring intent into a concise 2-3 sentence monitor goal.
• State what should trigger an alert, restate any scope the user gave, and include intent-specific exclusions only when obvious from the user's 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; do not repeat it in every goal.
• If the user is vague, keep the goal broad rather than guessing exclusions. If the user asks for broad monitoring or "any change", preserve that and do not add exclusions that hide changes.
• If the user says they do not care about something, include that explicitly. It is okay to ask whether they want to ignore specific noise when it is likely to matter.
• Do not invent page-specific sections, thresholds, entities, or business rules unless the user mentioned them.

Query guidance (web monitors): queries control recall (what search retrieves) and goal controls precision (which results alert) — tune both.

• Write keywords, not sentences: OpenAI new model release, not tell me when OpenAI releases a new model.
• Quote multi-word entities ("Llama 4"); group synonyms with OR (launch OR release OR announcement).
• Keep each query tight (~2-6 terms). One broad query usually beats several narrow ones — extra queries split the maxResults budget. Use one query per distinct entity; do not emit one per facet of a single subject.
• Keep site: operators out of queries — use includeDomains / excludeDomains.
• A healthy web monitor mostly returns new: 0 and alerts only on genuinely new, on-goal results. Many ignored results ⇒ queries too broad (tighten them); nothing for long stretches ⇒ queries too narrow or window too tight (broaden); dismissed alerts ⇒ goal too broad (add an intent-specific Ignore). Aim for high precision with enough recall.

Full body requests require: name, schedule (with cron or text), and targets (one or more { type: 'scrape', urls: [...] }, { type: 'crawl', url: '...' }, or { type: 'search', queries: [...], searchWindow?, maxResults?, includeDomains?, excludeDomains? }). Optional: goal (required when any search target is present), judgeEnabled, webhook, notification, retentionDays.

Markdown-mode (default): Each check produces a unified text diff of the page's markdown. No extra configuration needed.

json
{
  "name": "fir

[...]