What can you do with this server?

The Bright Data MCP server provides real-time web data access and extraction for AI applications, enabling seamless web navigation and scraping without restrictions. With this server, you can: * Search Engine Scraping: Extract results from Google, Bing, or Yandex in markdown format * Webpage Scraping: Scrape any webpage in HTML or markdown format, bypassing bot detection and CAPTCHA * Structured Data Extraction: Retrieve structured data from sites like Amazon (products, reviews) and LinkedIn (profiles, company pages) * Browser Automation: Navigate, click, type, and interact with webpages in a scraping session * Advanced Navigation: Wait for specific elements, go back/forward in browser history, and extract links * Capture Content: Take screenshots and extract text or HTML from webpages * Bypass Geo-restrictions: Access location-restricted content

Which integrations are available for this server?

Enables searching for web content like upcoming movie releases Supports OpenAI Agents to access and utilize web data through the MCP server Allows retrieving market data and company information Provides access to Wikipedia content including the article of the day

How do I use Bright Data MCP?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Bright Data MCP what are the current prices for iPhone 15 on Amazon?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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Bright Data MCP

Official

by brightdata

Overview Schema Source Related Servers Discussions Admin

JavaScript

Hybrid

Overview

The Bright Data MCP server gives AI agents real-time access to public web data. It exposes 32 tools covering:

Web search — Google, Bing, and Yandex results as structured data
Page scraping — any URL as Markdown or HTML, with bot detection, CAPTCHA solving, and proxy rotation handled automatically on every request
Structured data (Web Scraper API) — clean JSON from 1,200+ pre-built scrapers: Amazon, LinkedIn, Instagram, TikTok, YouTube, X, Reddit, Facebook, Crunchbase, Zillow, npm, PyPI and more, without parsing HTML. Six tools find and run any of them
Browser automation — navigate, click, type, screenshot, and read pages in a remote browser session
LLM response collection — send prompts to ChatGPT and Perplexity and get their answers back as structured data

Every request is routed through Bright Data's unblocking infrastructure, so pages that block ordinary HTTP clients (bot detection, CAPTCHAs, rate limits, geo-restrictions) return normally. No proxy setup, no headless browser maintenance, no retry logic to write.

Two deployment options: a hosted remote server (one URL, no installation) or a local instance via npx @brightdata/mcp.

Related MCP server: MCP Fetch

Quick Start

Hosted server — no installation. Add this URL to your MCP client:

https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN_HERE

Get your API token from your Bright Data account settings. New accounts get 5,000 free requests per month.

Optional URL parameters:

Parameter	Description	Example
`groups=<ids>`	Enable specific tool groups	`...&groups=scrapers,browser`
`tools=<names>`	Enable specific tools only	`...&tools=search_engine,scrape_as_markdown`

Go to: Settings → Connectors → Add custom connector
Name: Bright Data
URL: https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN
Click "Add"

Or run locally:

{
  "mcpServers": {
    "Bright Data": {
      "command": "npx",
      "args": ["@brightdata/mcp"],
      "env": {
        "API_TOKEN": "<your-api-token-here>"
      }
    }
  }
}

claude mcp add --transport http brightdata "https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN"

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "brightdata": {
      "url": "https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN"
    }
  }
}

Add to .vscode/mcp.json:

{
  "servers": {
    "brightdata": {
      "type": "http",
      "url": "https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN"
    }
  }
}

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "brightdata": {
      "serverUrl": "https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN"
    }
  }
}

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "brightdata": {
      "httpUrl": "https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN"
    }
  }
}

Add to your Zed settings:

{
  "context_servers": {
    "brightdata": {
      "url": "https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN"
    }
  }
}

Go to Settings > MCP Servers > Add MCP Server and add:

{
  "brightdata": {
    "url": "https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN"
  }
}

For any client that supports local MCP servers:

{
  "mcpServers": {
    "Bright Data": {
      "command": "npx",
      "args": ["@brightdata/mcp"],
      "env": {
        "API_TOKEN": "<your-api-token-here>"
      }
    }
  }
}

Pricing and Free Tier

Every account includes a recurring monthly free tier. No credit card or commitment required to start.

5,000 free requests per month, renewing on the 1st of each month. Unused requests don't roll over. For team accounts, the free tier is shared across all users in the account.

What's included free:

Fetch any webpage and extract as Markdown
Access to 60+ pre-built scrapers for popular domains
Web search (Google, Bing, Yandex)
Web unlocking (bot detection bypass, CAPTCHA solving, proxy rotation)
Browser automation
Geo-targeting

Beyond the free tier — pay as you go, no commitment:

	Search, Scrape & Extract	Browser Navigation
Pay as you go	$1.50 / 1K results	$8 / GB

When free requests run out, requests stop. No surprise charges — unless you have deposited funds
Adding a credit card is a verification step only; you are not charged unless your free tier is exhausted and you have funds deposited
Set a spend cap in the control panel so pay-as-you-go usage never exceeds your budget

Full pricing, volume plans and enterprise →

Use Cases

Real-time research

Answer questions using live web data instead of training data. Search, then read the sources.

Task	Tools
Search the web for current information	`search_engine`, `search_engine_batch`
Read a specific page as clean Markdown	`scrape_as_markdown`, `scrape_batch`
Find the most relevant sources for a research question, ranked by AI relevance score	`discover`

Example prompts: "What's Tesla's current stock price?", "Get today's weather forecast for New York", "Find the most cited sources on EU AI regulation from the last 6 months".

E-commerce intelligence

Read product data as structured JSON: price, availability, rating, review count, seller, images.

Task	Tools
Products, reviews and search results from Amazon, Walmart, eBay, Best Buy, Etsy, Home Depot, Zara and other stores	`search_scrapers` → `get_scraper_details` → `run_scraper`
Cross-retailer price view	the same flow with the Google Shopping scraper

Example prompts: "Compare this laptop's price on Amazon vs Walmart vs Best Buy", "Get the rating and review count for ASIN B0D2Q9397Y", "Is this product in stock?".

Market and competitor analysis

Build competitor profiles from live data: funding, headcount, hiring, customer reviews, pricing pages.

Task	Tools
Company funding, size, employees, job postings (Crunchbase, ZoomInfo, LinkedIn)	`search_scrapers` → `run_scraper`
Customer sentiment (Google Maps, Facebook, app store reviews)	`search_scrapers` → `run_scraper`
Competitor pricing pages	`scrape_as_markdown`, `scrape_batch`
Market discovery	`search_engine_batch`, `discover`

Example prompt: "Analyze Notion as a competitor: pricing, funding, hiring focus, and what customers complain about".

AI agents with reliable web access

Replace built-in fetch/search tools that get blocked on protected sites. Every request goes through unblocking infrastructure, so agents don't fail on bot detection, CAPTCHAs, or geo-restrictions.

Task	Tools
Drop-in replacement for built-in web search	`search_engine`
Drop-in replacement for built-in URL fetch	`scrape_as_markdown`
Parallel data collection (10 at a time)	`search_engine_batch`, `scrape_batch`
Interactive sites (login walls, infinite scroll, dynamic content)	`scraping_browser_*` (14 tools)
Structured JSON from any page, no schema needed	`extract`

Coding agents

Package registry data on demand — no scraping, no stale caches.

Task	Tools
npm and PyPI package version, README, metadata	`run_scraper` with the npmjs or pypi scraper (find them with `search_scrapers`)
Read files from GitHub repositories	`run_scraper` with the GitHub repository scraper

Example prompts: "What's the latest version of express on npm?", "Get the README for the langchain-brightdata PyPI package".

GEO and brand visibility

Send prompts to major LLMs and get their answers back as structured data. Measure how AI assistants describe your brand, which sources they cite, and what they recommend — the feedback loop for Generative Engine Optimization.

Task	Tools
ChatGPT answers with citations and recommendations	`chatgpt_ai_insights`
Perplexity answers with sources	`perplexity_ai_insights`

Example prompt: "Ask ChatGPT and Perplexity 'what is the best proxy provider' and compare how each one ranks us".

Structured data from every major platform: profiles, posts, comments, engagement metrics. Each platform has scrapers per data type (LinkedIn profiles, Instagram reels, TikTok comments, ...); find them with search_scrapers and run them with run_scraper.

Platform	Examples of scrapers
LinkedIn	person profiles, company profiles, job listings, posts, people search
Instagram	profiles, posts, reels, comments
TikTok	profiles, posts, shop, comments
Facebook	profiles, posts, marketplace listings, company reviews, events
YouTube	videos, channels, comments
X (Twitter)	posts, posts by profile
Reddit	posts, comments

Example prompt: "Get the last 10 posts from this TikTok profile and summarize the engagement".

Content creation and academic research

Gather source material from many pages at once, filtered by recency and relevance.

Task	Tools
Collect multiple sources in one call	`scrape_batch` (up to 10 URLs)
Find sources by topic with date filtering	`discover` with `start_date` / `end_date`
News and finance data	`search_engine` with news queries, `run_scraper` with the Yahoo Finance scraper

How It Compares

Capability	Bright Data MCP	Typical web MCP servers
Total tools	32	2–10
Platform-specific structured JSON extractors	1,200+ pre-built scrapers across e-commerce, social, business, finance, travel, app stores, through 6 tools	Rare; generic scraping only
Unblocking (bot detection bypass, CAPTCHA solving, proxy rotation)	Built into every request	Usually none; blocked on protected sites
Search engines	Google, Bing, Yandex	Usually one
AI-relevance-ranked search with intent	Yes (`discover`)	Not offered
Browser automation	14 tools, remote browser, no local setup	Limited or none
LLM response collection (ChatGPT, Perplexity)	Yes	Not offered
Package registry data (npm, PyPI)	Yes	Not offered
Batch operations	10 searches or 10 scrapes per call	Usually single-request only
Geo-targeting	Yes	Limited or none
Free tier	5,000 requests/month, browser automation included, no credit card	Varies; often rate-limited keyless access

Tool Selection: Groups

Tools are organized into groups so you only load what you need. Fewer tools means less context for your agent to process.

GROUPS enables tool bundles. Comma-separated: GROUPS="scrapers,browser" (local) or &groups=scrapers,browser (hosted URL)
TOOLS adds individual tools on top: TOOLS="extract,scrape_as_html"
Base tools are always enabled: search_engine, search_engine_batch, scrape_as_markdown, scrape_batch, discover
Group ID custom is reserved; use TOOLS for individual picks

Group ID	Contents	Tool count
`scrapers`	Search and run any pre-built Web Scraper API scraper (Amazon, LinkedIn, TikTok, npm, ...)	6
`browser`	Remote browser automation	14
`geo`	ChatGPT and Perplexity response collection	2
`advanced_scraping`	Batch tools, HTML scraping, AI extraction, session stats	5

Older group IDs (ecommerce, social, business, finance, research, app_stores, travel, code) still work and enable the scrapers tools; social and business also add search_dataset and list_dataset_fields. Old web_data_* names in TOOLS enable the scrapers tools too.

Configuration examples

Local server with browser automation and AI extraction:

{
  "mcpServers": {
    "Bright Data": {
      "command": "npx",
      "args": ["@brightdata/mcp"],
      "env": {
        "API_TOKEN": "<your-api-token-here>",
        "GROUPS": "browser,advanced_scraping",
        "TOOLS": "extract"
      }
    }
  }
}

Coding agent setup (Claude Code / Cursor / Windsurf) — npm, PyPI and GitHub data through the pre-built scrapers:

{
  "mcpServers": {
    "Bright Data": {
      "command": "npx",
      "args": ["@brightdata/mcp"],
      "env": {
        "API_TOKEN": "<your-api-token-here>",
        "GROUPS": "scrapers"
      }
    }
  }
}

Tools Reference (32 Tools)

Which tool to use

Known URL, need the content: scrape_as_markdown. Multiple URLs (up to 10): scrape_batch
Need to find information: search_engine. Multiple queries (up to 10): search_engine_batch
Deep research or RAG, need relevance-ranked sources: discover with an intent
Structured data from a known platform (Amazon, LinkedIn, TikTok, npm, PyPI, etc.): search_scrapers → get_scraper_details → run_scraper — returns clean JSON, faster and more reliable than scraping the same page
Structured JSON from a page no scraper covers: extract
Raw HTML: scrape_as_html
Page requires interaction (click, type, scroll, login): scraping_browser_* tools
How ChatGPT/Perplexity answer a prompt: chatgpt_ai_insights / perplexity_ai_insights

Notes on scraper runs:

Return structured JSON, billed per record returned
Records with an error field explain inputs that failed, e.g. a URL of the wrong type
Results can be large. Keep limit_per_input low for discover_by_* methods and run bulk collection in a subagent where your framework supports it, so records don't flood the main context window
If a run fails, scrape_as_markdown works on the same URL as a fallback

Tool	Description	Group
`search_engine`	Search Google, Bing, or Yandex. Google returns JSON (URL, title, description); Bing and Yandex return Markdown. Paginate with the `cursor` parameter	always enabled
`search_engine_batch`	Up to 10 search queries in one call	always enabled
`scrape_as_markdown`	Any URL as Markdown. Bot protection and CAPTCHA handled automatically	always enabled
`scrape_batch`	Up to 10 URLs in one call; returns an array of URL/content pairs in Markdown	always enabled
`discover`	AI-relevance-ranked web search. Returns scored results (title, description, URL, relevance score). Supports intent-based ranking, geo-targeting, date filtering, keyword filtering	always enabled
`scrape_as_html`	Any URL as raw HTML	`advanced_scraping`
`extract`	Scrape a page and convert it to structured JSON using AI, with an optional custom extraction prompt	`advanced_scraping`
`session_stats`	Tool usage counts for the current session	`advanced_scraping`

Search and run any of Bright Data's 1,200+ pre-built scrapers. The scraper list is loaded from docs.brightdata.com/scrapers.json and refreshed daily. Available on both the hosted and the local server. Enabled by default when no tools or groups are selected; otherwise add the scrapers group (&groups=scrapers on the hosted URL, GROUPS=scrapers locally).

Typical flow: search_scrapers → get_scraper_details → run_scraper. Runs are billed per record; discover_by_* methods collect up to limit_per_input records per input (default 10).

Tool	Description	Group
`search_scrapers`	Find scrapers by site or data type (e.g. "amazon reviews", "linkedin.com"). Returns dataset ID, name, domain and collection methods	default / `scrapers`
`get_scraper_details`	Description, input fields, example input and main output fields for one scraper method	default / `scrapers`
`run_scraper`	Run a scraper method. Waits up to 45 seconds; if still running, returns a `snapshot_id` to check later	default / `scrapers`
`get_scraper_progress`	Status of a run: starting, running, ready, failed or canceled	default / `scrapers`
`get_scraper_results`	Records of a finished run	default / `scrapers`
`refresh_scrapers`	Reload the scraper list now instead of waiting for the daily refresh	default / `scrapers`

Remote browser session. Typical sequence: navigate → snapshot → interact by ref → extract or screenshot.

Tool	Description
`scraping_browser_navigate`	Open or reuse a browser session and navigate to a URL
`scraping_browser_go_back`	Navigate back
`scraping_browser_go_forward`	Navigate forward
`scraping_browser_snapshot`	ARIA snapshot of the page listing interactive elements with refs. Required before ref-based actions
`scraping_browser_click_ref`	Click an element by ref from the latest snapshot
`scraping_browser_type_ref`	Type into an element by ref; optionally press Enter to submit
`scraping_browser_fill_form`	Fill several form fields by ref in one call
`scraping_browser_screenshot`	Screenshot of the current page; optional `full_page`
`scraping_browser_get_text`	Text content of the page body
`scraping_browser_get_html`	HTML of the current page
`scraping_browser_scroll`	Scroll to the bottom of the page
`scraping_browser_scroll_to_ref`	Scroll an element into view
`scraping_browser_wait_for_ref`	Wait for an element to become visible, with optional timeout
`scraping_browser_network_requests`	Network requests since page load: method, URL, status

Refs come from the latest snapshot. If the page changes after a click or navigation, take a new snapshot before the next ref-based action. For static pages, scrape_as_markdown is faster and cheaper than a browser session.

Tool	Input	Returns
`chatgpt_ai_insights`	Prompt	ChatGPT's answer as Markdown
`perplexity_ai_insights`	Prompt	Perplexity's answer as Markdown

Use for Generative Engine Optimization (tracking how LLMs describe your brand) and LLM-as-a-judge workflows.

Filter search over stored LinkedIn datasets; returns matching records at once, with no scraper run.

Tool	Description	Group
`list_dataset_fields`	Filterable fields of a dataset (name, type, description). Call before `search_dataset`	`social`, `business`
`search_dataset`	Records matching a filter, up to 10 per call, with a cursor for more	`social`, `business`

Full tool reference in the docs →

Agent Skills

Ready-to-use skills that teach your agent how to use this MCP server correctly. The full collection lives at github.com/brightdata/skills — 21 skills covering MCP orchestration, competitive intelligence, price comparison, brand listening, SEO audits, scraper building, RAG pipelines, and more.

Three of the highest-impact skills:

Skill	What it does
bright-data-mcp	Makes Bright Data MCP the default for all web data operations, replacing WebFetch, WebSearch, and other built-in web tools that fail on bot detection
competitive-intel	Competitor snapshots, pricing comparison, review mining, hiring signals, content/SEO analysis, and market landscape maps from live web data
price-comparison	Resolves a product across Amazon, Walmart, eBay, Best Buy, and Google Shopping and names the cheapest in-stock option

Browse all 21 skills →

Configuration

Basic setup (local)

{
  "mcpServers": {
    "Bright Data": {
      "command": "npx",
      "args": ["@brightdata/mcp"],
      "env": {
        "API_TOKEN": "your-token-here"
      }
    }
  }
}

Advanced configuration

{
  "mcpServers": {
    "Bright Data": {
      "command": "npx",
      "args": ["@brightdata/mcp"],
      "env": {
        "API_TOKEN": "your-token-here",
        "RATE_LIMIT": "100/1h",
        "WEB_UNLOCKER_ZONE": "custom",
        "BROWSER_ZONE": "custom_browser",
        "POLLING_TIMEOUT": "600"
      }
    }
  }
}

Environment variables

Variable	Description	Default	Example
`API_TOKEN`	Your Bright Data API token (required)	-	`your-token-here`
`RATE_LIMIT`	Custom rate limiting	unlimited	`100/1h`, `50/30m`
`WEB_UNLOCKER_ZONE`	Custom Web Unlocker zone name	`mcp_unlocker`	`my_custom_zone`
`BROWSER_ZONE`	Custom Browser zone name	`mcp_browser`	`my_browser_zone`
`POLLING_TIMEOUT`	Timeout for `discover` polling (seconds). Each second = 1 polling attempt	`600`	`300`, `1200`
`CONTENT_GATE`	When page content returned by a data tool looks like instructions to change the MCP configuration, the server asks the user (via MCP elicitation) before returning it. Set to `off` to disable	`on`	`off`
`CONTENT_GATE_TIMEOUT`	Seconds to wait for the user's answer before withholding the content	`120`	`60`, `300`
`BASE_TIMEOUT`	Request timeout for base tools in seconds (search and scrape)	No limit	`60`, `120`
`BASE_MAX_RETRIES`	Max retries for base tools on transient errors (0-3)	`0`	`1`, `3`
`GROUPS`	Comma-separated tool group IDs	-	`scrapers,browser`
`TOOLS`	Comma-separated individual tool names	-	`extract,scrape_as_html`

Content gate

scrape_as_markdown, search_engine, extract and their batch variants return whatever a web page says. If that content contains text that reads like instructions to change your MCP configuration — register a server, run an npx -y package, edit the MCP config file — the server pauses and asks you, through your MCP client, before returning it. Approve and the content is returned unchanged; decline and it is withheld with a short explanation to the assistant. Pages that do not contain such text are returned exactly as before, with no prompt. For a batch call, only the matching items are withheld.

Pages that are about MCP configuration (documentation, READMEs) will naturally prompt. Clients that do not support MCP elicitation get the matching content withheld; set CONTENT_GATE=off if you accept the risk. While the server waits for your answer it reports progress; clients that do not extend their tool timeout on progress will cap the wait at their own limit — CONTENT_GATE_TIMEOUT is the knob on the server side. Browser tools are not covered.

Documentation

Resource	Link
API documentation	docs.brightdata.com/ai/mcp-server/overview
Full tools reference	docs.brightdata.com/ai/mcp-server/tools
Agent skills	github.com/brightdata/skills
Usage examples	examples
Changelog	CHANGELOG.md

Troubleshooting

"spawn npx ENOENT" error

Install Node.js, or use the full path to node:

"command": "/usr/local/bin/node"  // macOS/Linux
"command": "C:\\Program Files\\nodejs\\node.exe"  // Windows

Timeouts on complex sites

Increase the timeout in your client settings to 180s.

Authentication issues

Verify your API token is valid and has the required permissions. Tokens are managed in account settings.

run_scraper returns errors or no data

Records with an error field explain inputs that failed. Compare your input with sample_input from get_scraper_details (e.g., Amazon product URLs contain /dp/, Walmart ones /ip/) and check the page is publicly accessible. scrape_as_markdown works on the same URL as a fallback.

Remote server connection fails

Check your internet connection and firewall settings.

Contributing

Please follow Bright Data's coding standards.

Support

Channel	Link
GitHub issues	github.com/brightdata-com/brightdata-mcp/issues
Documentation	docs.brightdata.com/ai/mcp-server/overview
Email	support@brightdata.com

License

MIT © Bright Data Ltd.

Available Tools

5 tools

discoverA

Read-only

Search the web and rank results by AI-driven relevance. Returns scored results with title, description, and URL. Supports intent-based ranking, geo-targeting, date filtering, and keyword filtering.

ParametersJSON Schema

Name	Required	Description
`city`	No	City for localized results (e.g., "New York", "Berlin")
`query`	Yes	The search query
`intent`	No	Describes the specific goal of the search to help the AI evaluate and rank result relevance.If not provided, the query string is used as the intent
`country`	No	2-letter ISO country code for localized results (e.g., "US", "GB", "DE")
`end_date`	No	Only content updated until this date (YYYY-MM-DD)
`language`	No	Language code (e.g., "en", "es", "fr")
`start_date`	No	Only content updated from this date (YYYY-MM-DD)
`num_results`	No	Exact number of search results to return
`filter_keywords`	No	Keywords that must appear in search results
`remove_duplicates`	No	Remove duplicate results (default: true)

TDQS

A3.9/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint (true) and openWorldHint (true), confirming safe read from external web. The description adds value by detailing AI-driven ranking, intent-based ranking, geo-targeting, and filtering, which are behavioral traits not fully captured by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states purpose and return type, second lists features. No fluff, front-loaded, and every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 10 parameters and no output schema, the description covers key aspects (scored results, features) but lacks details on default number of results, scoring mechanics, or error handling. Adequate but with gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds minimal extra meaning by grouping features (e.g., 'geo-targeting' maps to city/country) but does not elaborate on parameter usage beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs web search with AI-driven relevance ranking and returns scored results with title, description, and URL. It lists supported features (intent, geo, date, keyword), distinguishing it from siblings like search_engine and scrape tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through features but does not explicitly state when to use this tool vs alternatives like search_engine or scrape_as_markdown. No when-not-to or exclusions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

scrape_as_markdownB

Read-only

Scrape a single webpage URL with advanced options for content extraction and get back the results in MarkDown language. This tool can unlock any webpage even if it uses bot detection or CAPTCHA.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes

TDQS

B3.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and open-world behavior. The description adds a key behavioral claim: the ability to bypass bot detection and CAPTCHA, which is valuable information not covered by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no wasted words. The claim about unlocking any webpage is front-loaded but could be more precise about scope.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description covers the main purpose and a key feature (bot detection bypass). However, it lacks details on return structure, error handling, or rate limits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description only repeats 'webpage URL' without adding format or constraints beyond the schema's `format: uri`. No detail on the single parameter's meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scrapes a single webpage URL and returns MarkDown, distinguishing it from siblings like scrape_batch. However, it mentions 'advanced options' that are not reflected in the input schema, slightly reducing clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for single URL scraping with bot detection bypass, but does not explicitly state when to use vs. siblings (e.g., scrape_batch for multiple URLs) or list any prerequisites or limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

scrape_batchA

Read-only

Scrape multiple webpages URLs with advanced options for content extraction and get back the results in MarkDown language. This tool can unlock any webpage even if it uses bot detection or CAPTCHA.

ParametersJSON Schema

Name	Required	Description	Default
`urls`	Yes	Array of URLs to scrape (max 5)

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds value beyond annotations by noting MarkDown output and the ability to unlock webpages with bot detection/CAPTCHA. No contradictions with readOnlyHint and openWorldHint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with core function and output format, no unnecessary words. Efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one parameter and no output schema, the description covers the main function and key capability. The vague phrase 'advanced options for content extraction' is a minor gap, but otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single parameter 'urls', so baseline is 3. The description does not add significant meaning beyond the schema, merely restating 'multiple webpages URLs'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (scrape), resource (webpages), and output format (MarkDown). It distinguishes from siblings like scrape_as_markdown by specifying batch operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for multiple URLs and mentions bypassing bot detection, providing clear context. However, it lacks explicit when-not-to-use or alternative tool guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_engineB

Read-only

Scrape search results from Google, Bing or Yandex. Returns SERP results in JSON or Markdown (URL, title, description),Ideal forgathering current information, news, and detailed search results.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`cursor`	No	Pagination cursor for next page
`engine`	No		google
`geo_location`	No	2-letter country code for geo-targeted results (e.g., "us", "uk")

TDQS

B3.4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint. The description adds output format and fields but does not disclose rate limits, auth requirements, or limitations. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with a typo ('forgathering') and missing punctuation ('results,Ideal'). Could be more concise and better structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 parameters and no output schema, the description lacks explanation of pagination, engine selection, and geo-targeting. Output format is mentioned but not how to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50% (cursor and geo_location have descriptions). The tool description does not elaborate on any parameters, failing to compensate for undocumented query and engine parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scrapes search results from Google, Bing, or Yandex and returns SERP data in JSON/Markdown. It distinguishes itself from sibling tools like search_engine_batch (batch variant) and scrape_as_markdown (scrapes a single page).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates it is ideal for gathering current information, news, and detailed search results but does not explicitly contrast with siblings or specify when to avoid using it. No mention of alternatives or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_engine_batchA

Read-only

Run multiple search queries simultaneously. Returns JSON for Google, Markdown for Bing/Yandex.

ParametersJSON Schema

Name	Required	Description	Default
`queries`	Yes

TDQS

A3.7/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that output format differs by engine (JSON for Google, Markdown for Bing/Yandex), which is useful but does not disclose other behavioral details like rate limits or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loading purpose and format differences. Every sentence adds value with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (batch, multiple engines, optional parameters, varying output formats), the description lacks essential details about how to structure queries and interpret results. No output schema exists to compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, and the tool description provides no explanation of the parameters (e.g., that queries is an array of objects with fields like cursor, engine, geo_location). The agent must rely solely on the schema, which is insufficient for correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it runs multiple search queries simultaneously, which is a specific verb+resource. It distinguishes from sibling tools like search_engine (single query) and scrape tools by being a batch search operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for batching multiple queries, but lacks explicit when-to-use vs when-not-to-use or alternatives. It is clear enough to guide the agent to use this over search_engine for multiple queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

5 tool updatesv2.9.3May 11, 2026
- Addeddiscover
- Addedscrape_as_markdown
- Addedscrape_batch
- Addedsearch_engine
- Addedsearch_engine_batch

Deploy Server

Give agents eyes on the webHosted screenshot MCP. Send a URL, get a pixel-perfect image. $0.002 per grab. Works with Claude, Cursor, and Codex.grabbit.liveAd

TDQS

A3.6/5.0

Scored 2026-06-04 09:55 across 5 tools

Disambiguation4/5

Most tools have distinct purposes: discover for AI-ranked web search, scrape for webpage content, and search_engine for SERP results. However, discover and search_engine both involve search, potentially causing confusion.

Naming Consistency3/5

Naming mixes patterns: 'discover' is a single verb, 'scrape_as_markdown' is a phrase, 'search_engine' is a noun. Inconsistent but still readable.

Tool Count4/5

With 5 tools covering web search, scraping (single/batch), and engine search (single/batch), the count is appropriate for a focused data extraction tool.

Completeness4/5

Covers key data extraction needs: AI search, webpage scraping, and SERP scraping. Minor gap: no tool for updating or deleting data, but that is outside typical scope.

Maintenance

ActivityActive

ResponsivenessUnresponsive

Resources

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Overview

Quick Start

Pricing and Free Tier

Use Cases

Real-time research

E-commerce intelligence

Market and competitor analysis

AI agents with reliable web access

Coding agents

GEO and brand visibility

Social media monitoring

Content creation and academic research

How It Compares

Tool Selection: Groups

Configuration examples

Tools Reference (32 Tools)

Which tool to use

Agent Skills

Configuration

Basic setup (local)

Advanced configuration

Environment variables

Content gate

Documentation

Troubleshooting

"spawn npx ENOENT" error

Timeouts on complex sites

Authentication issues

run_scraper returns errors or no data

Remote server connection fails

Contributing

Support

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers