Learn how to build a web research agent using Agenite’s MCP integration to access web data
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:
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:
Before building the web research agent, you’ll need:
First, install the required packages:
The first step is to create an MCP client that will connect to the MCP fetch server:
Now let’s build the agent that will use the MCP tools:
Let’s use our agent to answer a research question:
Here’s the complete example for a web research agent using MCP:
For a more interactive experience, you can use Agenite’s streaming capabilities:
In this guide, we’ve built a web research agent using Agenite’s MCP integration. This agent can:
The Model Context Protocol provides a standardized way to connect our agent to web data sources, making it easy to build powerful web research capabilities without having to implement complex web scraping or API integration logic.
For more advanced usage and a deeper understanding of MCP, check out the MCP package documentation.
View the full example on GitHub.
Learn how to build a web research agent using Agenite’s MCP integration to access web data
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:
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:
Before building the web research agent, you’ll need:
First, install the required packages:
The first step is to create an MCP client that will connect to the MCP fetch server:
Now let’s build the agent that will use the MCP tools:
Let’s use our agent to answer a research question:
Here’s the complete example for a web research agent using MCP:
For a more interactive experience, you can use Agenite’s streaming capabilities:
In this guide, we’ve built a web research agent using Agenite’s MCP integration. This agent can:
The Model Context Protocol provides a standardized way to connect our agent to web data sources, making it easy to build powerful web research capabilities without having to implement complex web scraping or API integration logic.
For more advanced usage and a deeper understanding of MCP, check out the MCP package documentation.
View the full example on GitHub.