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CrawlForge MCP

Use Cases

Feed your AI agents live web data with structured extraction and multi-source research. Build pipelines with 31 MCP tools, from fetch_url to deep_research.
Estimated cost: ~12 credits per research task

01Quick Answer

Connect CrawlForge to your agent over MCP and it gains 31 web tools it can call directly. Use deep_research (10 credits) for multi-source analysis with conflict detection and extract_content (2 credits) for clean, structured output -- no custom scrapers to build or maintain. A typical research task runs about 12 credits.

02The brief

The Problem

AI agents reason well but are blind to live web data. Building custom scrapers for every data source is slow, fragile, and expensive to maintain. Agents need structured, real-time information to make accurate decisions.

The Solution

CrawlForge gives your agents direct access to web data through MCP. Use deep_research for multi-source analysis with conflict detection, and extract_content for clean, structured output -- no custom scrapers needed.

03In code

Code Example

ai-agent-data-pipelines.js
// AI agent fetches and synthesizes live market dataconst research = await mcp.deep_research({  query: "Latest funding rounds in AI infrastructure startups Q1 2026",  sources: 5,  conflict_detection: true,}); // Extract structured data from a specific sourceconst content = await mcp.extract_content({  url: research.sources[0].url,  format: "markdown",}); console.log(research.summary);console.log(content.main_content);

04The pipeline

Tools Used

12◆ credits
Estimated cost: ~12 credits per research task
  1. https://…
  2. 01deep_research 10 credits
  3. 02extract_content 2 credits
  4. JSON · markdown

05Questions

Frequently Asked Questions

01How do I give an AI agent live web data without building custom scrapers?

Connect CrawlForge over MCP and the agent gains 31 tools it can call directly. Use deep_research for multi-source analysis with conflict detection and extract_content for clean, structured output — no per-source scraper code to write or maintain.

02What is an AI agent data pipeline?

It is the flow that feeds an autonomous agent structured, real-time web data so it can reason on current facts. With CrawlForge the pipeline is just MCP tool calls: the agent searches, researches, and extracts on demand instead of relying on a stale training cutoff.

03How many credits does a typical agent research task cost?

About 12 credits — roughly 10 for one deep_research call plus 2 for an extract_content follow-up. Every plan including the free 1,000-credit tier can run it, and you pay per call, so cost scales with how much research the agent does.

04Does CrawlForge work with LangChain and LlamaIndex agents?

Yes. CrawlForge is MCP-native and also exposes a REST API, so it plugs into LangChain, LlamaIndex, the Vercel AI SDK, and any MCP client like Claude or Cursor. The agent auto-discovers all 31 tools once connected.

06Keep exploring

Related Use Cases

Real-Time Research Agents

Build AI agents that search the web, synthesize findings, and deliver up-to-date research. Every claim cites its source so answers stay verifiable.

deep_research 10 creditssearch_web 5 credits

AI Training Data Collection

Collect and structure large-scale web datasets for fine-tuning and training AI models. Crawl entire sites, extract clean text, and export training-ready data.

batch_scrape 5 creditsextract_content 2 credits

Start forging

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Every new account gets 1,000 free credits. No credit card required.

Start Free with 1,000 Credits