Building an AI Research Assistant with Claude and MCP

Imagine an AI research assistant that can:

• Search the web for relevant sources
• Extract and verify information from multiple websites
• Cross-reference facts for accuracy
• Synthesize findings into a coherent summary with citations

With Claude, the Model Context Protocol (MCP), and CrawlForge, you can build this in an afternoon. This guide walks you through the architecture, implementation, and production considerations.

The Vision: Research Like a Human

Traditional LLMs are limited to their training data. When you ask GPT-4 or Claude a question, they can only recall what they've seen before. But humans don't work that way—we search, read, verify, and synthesize new information.

An AI research assistant should:

1. Understand intent - Break down complex queries into searchable topics
2. Discover sources - Find relevant web pages, documentation, articles
3. Extract information - Pull out key facts, quotes, and data
4. Verify accuracy - Cross-check information across multiple sources
5. Synthesize results - Combine findings into a clear, cited answer

Let's build it.

Architecture Overview

Our research assistant has three layers:

┌─────────────────────────────────────────────────┐ │ LLM Layer (Claude/GPT-4) │ │ - Query understanding │ │ - Source relevance scoring │ │ - Information synthesis │ └─────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────┐ │ MCP Server (CrawlForge) │ │ - search_web (5 credits) │ │ - extract_content (2 credits) │ │ - deep_research (10 credits) │ └─────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────┐ │ Web Data Layer │ │ - Google Search results │ │ - Website content │ │ - Structured data │ └─────────────────────────────────────────────────┘

Data Flow:

1. User submits research query
2. LLM expands query into search terms
3. CrawlForge searches the web and extracts content
4. LLM verifies and synthesizes information
5. Return structured answer with citations

Setting Up the Project

We'll use TypeScript, Claude's API (or OpenAI), and CrawlForge MCP server.

Prerequisites

Initialize the Project

Environment Setup

Create .env:

Get your CrawlForge API key at crawlforge.dev/signup (1,000 free credits).

Implementing the Research Flow

1. Query Understanding

First, we need to expand user queries into effective search terms.

2. Web Search and Content Extraction

Next, we search for relevant sources and extract content.

Credit Cost:

• 3 search terms × 5 credits = 15 credits
• 15 sources × 2 credits = 30 credits
• Total: 45 credits per research query

3. Information Verification

Cross-reference facts across sources to verify accuracy.

What's Next?

Now that you've built a basic research assistant, you can:

1. Add streaming - Stream results as they're found for better UX
2. Store results - Save research to a database for later retrieval
3. Build a UI - Create a web interface with Next.js or React
4. Add webhooks - Get notified when research completes
5. Fine-tune prompts - Optimize for your specific use case

Resources

• CrawlForge API Docs
• Deep Research Tool
• Credit Optimization Guide

Start building: Get 1,000 free credits at crawlforge.dev/signup.