Overview
In this guide, we’ll build a web research agent that can search and fetch information from the web using the Model Context Protocol (MCP). Web research agents are perfect for gathering real-time information from websites, answering queries based on web content, and creating summaries of online resources. The following diagram illustrates the architecture of our web research agent:What is MCP?
Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools. The key benefits of using MCP for our web research agent:- Seamless integration with web data sources
- Standardized interface for fetching content
- Separation of concerns between the agent and the data fetching logic
- Ability to switch between different data sources without changing the agent code
Prerequisites
Before building the web research agent, you’ll need:- Node.js installed on your system
- Basic understanding of TypeScript and Agenite
- An API key for your preferred LLM provider (OpenAI, Anthropic, or others)
Installation
First, install the required packages:Setting up the MCP client
The first step is to create an MCP client that will connect to the MCP fetch server:Creating the web research agent
Now let’s build the agent that will use the MCP tools:Using the web research agent
Let’s use our agent to answer a research question:Complete example
Here’s the complete example for a web research agent using MCP:Advanced usage: streaming responses
For a more interactive experience, you can use Agenite’s streaming capabilities:Conclusion
In this guide, we’ve built a web research agent using Agenite’s MCP integration. This agent can:- Connect to MCP servers to access web data
- Search and retrieve information from websites
- Process and summarize web content
- Provide cited responses to research queries