Crawlbase MCP
MCP ServerFree** - Enables AI agents to access real-time web data with HTML, markdown, and screenshot support. SDKs: Node.js, Python, Java, PHP, .NET.
Capabilities11 decomposed
raw html fetching with javascript rendering
Medium confidenceFetches live web content as raw HTML with optional JavaScript execution via the Crawlbase API backend. The MCP server wraps Crawlbase's rendering infrastructure, supporting both static HTML requests (using CRAWLBASE_TOKEN) and JavaScript-rendered pages (using CRAWLBASE_JS_TOKEN). Requests are routed through a retry queue with exponential backoff for resilience against transient failures.
Integrates Crawlbase's production-grade proxy rotation and anti-bot evasion infrastructure directly into the MCP protocol, eliminating the need for agents to manage their own proxy pools or handle bot detection. Uses dual-token authentication (standard vs JS) to optimize cost by routing requests to appropriate backend infrastructure based on rendering requirements.
Provides JavaScript rendering and proxy rotation out-of-the-box (unlike Puppeteer/Playwright which require local infrastructure), while being simpler to deploy than self-hosted scraping stacks and offering geographic targeting that pure headless browser solutions don't provide.
markdown content extraction from web pages
Medium confidenceExtracts and converts web page content to clean, structured markdown format via the crawl_markdown tool. The MCP server delegates to Crawlbase's content processing pipeline, which parses HTML, removes boilerplate (navigation, ads, footers), and outputs markdown-formatted text suitable for LLM consumption. Supports the same rendering options as raw HTML fetching (JavaScript execution, proxy rotation, geographic targeting).
Provides server-side markdown extraction as part of the Crawlbase API rather than requiring client-side HTML parsing libraries. Combines JavaScript rendering, proxy rotation, and content extraction in a single API call, reducing latency and complexity compared to fetch-then-parse workflows.
Eliminates the need for separate HTML parsing libraries (Cheerio, jsdom) and handles JavaScript-rendered content natively, whereas client-side extraction tools require either headless browsers or static HTML parsing that fails on dynamic content.
multi-sdk support across node.js, python, java, php, and .net
Medium confidenceProvides official SDKs for multiple programming languages (Node.js, Python, Java, PHP, .NET) that wrap the Crawlbase API, enabling developers to use web scraping capabilities from their preferred language. Each SDK implements the same core functionality (HTML fetching, markdown extraction, screenshot capture) with language-idiomatic APIs. SDKs handle authentication, request formatting, and response parsing, abstracting away HTTP details.
Provides official SDKs for five major programming languages, enabling native integration without HTTP client boilerplate. Each SDK implements consistent APIs while respecting language conventions (e.g., async/await in Python, Promises in Node.js, Futures in Java).
More convenient than raw HTTP clients for each language; however, less flexible than direct API access for non-standard use cases or advanced features not exposed in SDKs.
webpage screenshot capture with rendering
Medium confidenceCaptures full-page or viewport screenshots of web content as base64-encoded images via the crawl_screenshot tool. The MCP server delegates to Crawlbase's screenshot infrastructure, which renders pages with JavaScript execution, applies geographic/device targeting, and returns PNG images encoded as base64 strings. Supports the same proxy rotation and anti-bot evasion as HTML fetching.
Provides server-side screenshot rendering with proxy rotation and geographic targeting, eliminating the need for agents to manage headless browser instances. Returns base64-encoded images directly compatible with vision-capable LLMs, enabling multi-modal analysis without intermediate image storage.
Simpler than deploying Puppeteer/Playwright infrastructure and includes anti-bot evasion that headless browsers lack; however, less flexible than client-side rendering for custom viewport sizes or interaction sequences.
dual-mode mcp server deployment (stdio and http)
Medium confidenceProvides two distinct operational modes for integrating web scraping into AI applications: stdio mode for direct subprocess communication with desktop AI clients (Claude, Cursor, Windsurf) via standard input/output streams, and HTTP mode for standalone network server deployments supporting multi-user access and custom integrations. Both modes expose the same three tools (crawl, crawl_markdown, crawl_screenshot) through the standardized MCP protocol, with authentication handled via environment variables (stdio) or HTTP headers (HTTP mode).
Implements both stdio and HTTP transport layers within a single codebase, allowing the same MCP server to operate as a subprocess for desktop clients or as a standalone network service. Uses StdioServerTransport from @modelcontextprotocol/sdk for stdio mode and Express.js for HTTP mode, providing flexibility for different deployment architectures without code duplication.
More flexible than single-mode MCP servers; supports both local desktop integration and cloud deployments from the same codebase. Simpler than building separate stdio and HTTP implementations while maintaining the standardized MCP protocol interface.
retry queue with exponential backoff for resilience
Medium confidenceImplements automatic retry logic with exponential backoff for failed Crawlbase API requests, improving reliability for transient failures (network timeouts, temporary API unavailability, rate limiting). The retry queue is integrated into the request processing pipeline, transparently retrying failed requests without exposing retry logic to the MCP client. Backoff strategy prevents overwhelming the Crawlbase API during outages.
Integrates retry logic at the MCP server level rather than requiring each client to implement its own retry strategy. Exponential backoff prevents thundering herd problems during API outages, and transparent retry handling keeps the MCP protocol interface simple.
Simpler than client-side retry logic and prevents duplicate retry attempts across multiple clients; however, lacks configurability compared to libraries like axios-retry or p-retry that expose backoff parameters.
geographic targeting and device emulation
Medium confidenceEnables requests to be routed through Crawlbase's proxy infrastructure with geographic targeting and device emulation, allowing agents to fetch content as if browsing from different regions or device types. Implemented via request parameters passed to the Crawlbase API, supporting country/region selection and device type emulation (mobile, desktop, tablet). Useful for testing geo-blocked content, mobile-specific rendering, or region-specific pricing.
Leverages Crawlbase's distributed proxy infrastructure to provide geographic targeting and device emulation as first-class request parameters, eliminating the need for agents to manage their own proxy pools or device emulation logic. Integrated directly into the MCP tool parameters.
Simpler than managing separate proxy providers or device emulation libraries; however, less flexible than Puppeteer/Playwright for custom device configurations or interaction sequences.
mcp protocol tool registration and schema validation
Medium confidenceRegisters the three web scraping tools (crawl, crawl_markdown, crawl_screenshot) as MCP tools with standardized JSON schemas, enabling AI clients to discover and invoke them through the MCP protocol. Each tool has a defined schema specifying input parameters (URL, optional request options) and output types (HTML, markdown, or base64 image). Schema validation ensures requests conform to expected types before being forwarded to Crawlbase API.
Implements MCP tool registration using the @modelcontextprotocol/sdk, providing standardized tool discovery and invocation for AI clients. Schemas are defined declaratively and validated automatically, reducing boilerplate compared to custom RPC implementations.
Standardized MCP protocol enables interoperability with multiple AI clients without custom integration code; however, less flexible than custom RPC implementations for non-standard tool patterns.
environment variable-based authentication and configuration
Medium confidenceManages Crawlbase API credentials and server configuration through environment variables (CRAWLBASE_TOKEN, CRAWLBASE_JS_TOKEN, MCP_SERVER_PORT, etc.), supporting both stdio and HTTP deployment modes. Environment variables are loaded at server startup and used to authenticate all requests to the Crawlbase API. Supports .env file loading via dotenv for local development.
Uses standard Node.js environment variable patterns with optional dotenv support, avoiding custom configuration file formats. Separates standard HTML tokens from JavaScript rendering tokens (CRAWLBASE_TOKEN vs CRAWLBASE_JS_TOKEN), allowing cost optimization by using appropriate token types for different request types.
Simpler than custom configuration file formats and aligns with cloud-native deployment practices; however, lacks runtime reconfiguration compared to config servers or dynamic secret management systems.
content processing pipeline with boilerplate removal
Medium confidenceImplements a server-side content processing pipeline that parses HTML, identifies and removes boilerplate content (navigation, footers, ads, sidebars), and extracts main article/content text. This pipeline is used by the crawl_markdown tool to produce clean, LLM-optimized output. The pipeline uses heuristic-based content detection to identify main content blocks and remove noise, improving signal-to-noise ratio for downstream LLM processing.
Delegates content extraction to Crawlbase's server-side pipeline rather than requiring client-side HTML parsing and heuristics. Produces markdown output optimized for LLM consumption, reducing token overhead compared to raw HTML.
Simpler than client-side extraction with libraries like Readability.js or Trafilatura, and produces markdown directly suitable for LLM input; however, less customizable than client-side libraries for specific content detection rules.
error handling and response normalization
Medium confidenceImplements standardized error handling across all three tools, catching Crawlbase API errors, network failures, and validation errors, and returning normalized error responses through the MCP protocol. Errors include HTTP status codes, error messages, and optional retry hints. Response normalization ensures consistent output format (HTML string, markdown string, or base64 image) regardless of underlying Crawlbase API response variations.
Normalizes errors from the Crawlbase API into standardized MCP error responses, abstracting API-specific error details from clients. Includes retry hints for transient failures, enabling intelligent retry logic in client applications.
Simpler error handling than custom error mapping in client code; however, less detailed than direct API error responses for debugging.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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AI-optimized web crawler — clean markdown extraction, JS rendering, structured output for RAG.
Firecrawl
API to turn websites into LLM-ready markdown — crawl, scrape, and map with JS rendering.
markdownify-mcp
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Oxylabs
** - Scrape websites with Oxylabs Web API, supporting dynamic rendering and parsing for structured data extraction.
Best For
- ✓AI agents building research tools that need live web data
- ✓LLM-powered applications requiring fresh HTML content for analysis
- ✓Teams building web intelligence systems with JavaScript-heavy targets
- ✓AI agents building content aggregation or research systems
- ✓LLM-powered document processing pipelines
- ✓Teams building knowledge extraction systems that need clean text input
- ✓Polyglot teams using multiple programming languages
- ✓Organizations with existing Python, Java, PHP, or .NET codebases
Known Limitations
- ⚠Requires valid Crawlbase API tokens (separate tokens for standard vs JS rendering)
- ⚠Subject to Crawlbase API rate limits and quota constraints
- ⚠Response latency depends on target page complexity and Crawlbase backend load
- ⚠No built-in caching — each request hits the live web
- ⚠Markdown extraction quality depends on page structure and Crawlbase's content detection heuristics
- ⚠Complex layouts with mixed content types may not convert perfectly to markdown
Requirements
Input / Output
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About
** - Enables AI agents to access real-time web data with HTML, markdown, and screenshot support. SDKs: Node.js, Python, Java, PHP, .NET.
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