Enable your AI agents to scrape and parse web content dynamically, including geo-restricted sites
io.github.Decodo/mcp-server (MCP)
Enable AI agents to scrape and parse web content dynamically, including geo-restricted sites. The server is represented as “io.github.Decodo/mcp-server” and is associated with scraping infrastructure and proxy management topics.
Connect LLMs and AI agents to live web data using MCP (Model Context Protocol). The Decodo MCP
Server lets you scrape websites, search engines, eCommerce platforms, and social media directly from
AI tools like Claude, Cursor, and Windsurf, all without the need to build scraping infrastructure
from scratch.
Structured outputs in JSON, Markdown, and screenshots
Server-side JavaScript rendering and anti-bot handling
125M+ IPs across 195+ locations
What is Decodo MCP server?
The Decodo MCP Server is a web scraping layer for AI agents. It connects MCP-compatible clients to
Decodo's Web Scraping API, enabling:
Web scraping for LLMs
Real-time data retrieval for RAG
AI agent browsing and research
Structured data extraction from dynamic websites
Instead of maintaining proxies, parsers, and retry logic, you get a single integration point for
reliable web data access.
Why use MCP for web scraping?
Model Context Protocol (MCP) is the emerging standard for connecting AI agents to external tools and
data sources. With MCP:
Agents can call tools dynamically
Integrations stay standardized
Workflows scale across environments
The Decodo MCP Server gives your agents reliable, production-ready web access through this standard.
Key features
Web scraping for AI agents, no infrastructure required. Scrape any website, including
JavaScript-heavy pages, without handling proxy rotation, CAPTCHA solving, or anti-bot systems.
Structured outputs for LLM workflows. Markdown (LLM-ready), JSON (for structured pipelines), and
screenshots (for visual context), built for RAG pipelines, AI research agents, and automation flows.
Built-in support for popular targets. Ready-made tools for Google and Bing (SERPs), Amazon,
Walmart, and Target (eCommerce), Reddit, TikTok, and YouTube (social media), and ChatGPT and
Perplexity (AI search).
Global proxy infrastructure. 125M+ residential IPs, 195+ geo-locations, and a 99.99% success
rate on even the most protected targets.
Modular MCP toolsets. Enable only what you need: web, search, ecommerce, social_media,
ai for cleaner tool selection and better agent performance.
Fast time to value. From API key to first scrape in minutes, no setup overhead.
Use cases
Use the Decodo MCP Server when you need web scraping for AI agents, structured data extraction at
scale, reliable access to dynamic websites, real-time data for RAG, or an alternative to building
scraping infrastructure from scratch. Common scenarios:
AI-powered web scraping – give LLMs the ability to collect fresh data instead of relying on
static training data.
RAG with live data – pull real-time Google, Bing, and AI search results into retrieval
pipelines.
eCommerce intelligence – track product prices, listings, and sellers across marketplaces
without getting blocked.
Social media data collection – gather posts, channels, and engagement data from Reddit,
TikTok, and YouTube.
Travel and price aggregation – build tools that collect live pricing and availability across
websites.
Quick start
Create a free account at dashboard.decodo.com – up to 2K
free requests, no credit card required.
Get your API key. Obtain a Web Scraping API basic authentication token from the dashboard.
Search and interact with ChatGPT for AI-powered responses and conversations.
Ask ChatGPT to explain quantum computing in simple terms.
perplexity
Search and interact with Perplexity for AI-powered responses and conversations.
Ask Perplexity what the latest trends in web development are.
Parameters
The following parameters are inferred from user prompts:
Parameter
Description
jsRender
Renders target URL in a headless browser.
geo
Sets the country from which the request will originate.
locale
Sets the locale of the request.
tokenLimit
Truncates the response content up to this limit. Useful if the context window is small.
prompt
Prompt to send to AI tools (chatgpt, perplexity).
search
Activates ChatGPT's web search functionality (chatgpt only).
xhr
When true, includes XHR or fetch responses in the scrape result where supported (e.g. tiktok_post).
deviceType
Device type to emulate (desktop, mobile, tablet). Parsed targets are stricter: Amazon tools are desktop-only so they omit it, and bing_search/google_ai_mode accept desktop and mobile only.
domain
Domain to use for the request (e.g., amazon.com, amazon.co.uk, bing.com).
pageFrom
Starting page number for pagination.
deliveryZip
ZIP code for delivery location (Target, Walmart).
storeId
Store ID for local inventory (Walmart).
country
Country for TikTok Shop requests.
limit
Maximum number of results to return (e.g., YouTube channel videos).
language_code
Language code for subtitles (e.g., en, es).
Examples
Scraping geo-restricted content
Query your AI agent with the following prompt:
code
Scrape peacock.com from a German IP address and tell me the pricing.
This prompt will say that peacock.com is geo-restricted. To bypass the geo-restriction:
code
Scrape peacock.com from a US IP address and tell me the pricing.
Limiting number of response tokens
If your agent has a small context window, the content returned from scraping will be automatically
truncated, in order to avoid context-overflow. You can increase the number of tokens returned within
your prompt:
code
Scrape hacker news, return 50k tokens.
If your agent has a big context window, tell it to return full content:
code
Scrape hacker news, return full content.
Use with Decodo agent skills
This server gives your agent the scraping tools. Decodo/agent-skills
teaches it when to reach for them, which surface to use, and how to call it — so the agent
picks the right tool and parameters on its own instead of guessing.
The skills route across the decodo CLI, this hosted MCP server, and the raw HTTP API, and fall back
to the MCP server automatically when no shell is available. Pairing the two gives your agent both the
execution layer (this server) and the decision layer (the skills) in a single setup.