The construction of server lace AI agents has recently become very easy. With the Lang Base Documents MCP Server, you can immediately connect AI models to the delated base documents – which makes the construction of composables, agent AI systems with memory without complex infrastructure.
In this guide, you will learn how to configure Langbus Documents within the cursor (AI Code Editor) make the MCP server, and a summary AI agent that uses Lang Base Documents as a direct, on -demand context.
Here’s what we will cover is:
Provisions
Before making an agent, you will need to set up some things and be ready to go some tools.
In this tutorial, I will use the following tech stack:
Langbus – Platform to build and deploy your server lace AI agents.
Lang Base SD’s – A type script AI SDK, JavaScript, type script, node. JS, Next Dot JS, Reacting, and is designed to work with so on.
Cursor – An AI code editor like the VS code.
You also need:
- Sign up At the Lang Base to access the API key.
What is the Model Context Protocol (MCP)?
Model Context Protocol (MCP) There is an open protocol that standards how applications provide external context to large language model (LLM). Through the MCP, developers can connect AI models to different tools and data sources such as documents, APIs, and databases.
MCP allows LLM to call customs tools (such as documents or API Explorer) during a conversation.
MCP General architecture
In the basic part, the MCP follows a client server architecture where the host application can connect with several servers.
This is a common architecture that looks like:
The model context allows the protocol architecture to securely connect AI clients (such as clouds, id, and developer tools) in real time with many local or remote data sources. MCP clients interact with one or more MCP servers, who work as a bridge for data – whether they are from local files, databases, or remote APIs.
This setup allows the AI model to retrieve the data in a direct, relevant context, without interruption, without directly embedded data in the model.
Anthropic’s role in launching MCP
Anthropic Introducing the MCP as part of their vision through default to the LLMS tool. The MCP was originally made to enhance the capabilities of the cloud, but now it is more widely available and with the help of a developer in a friendly environment like cursor and cloud desktop.
By standardizing how LLM work integrates tools, it makes it easy for MCP developers to increase the AI system without a custom plugin or API hex.
Cursor AI code editor
Cursor A developer is the First AI code editor that integrates LLMS (such as Claude, GPT, and more) directly into your IDE. The cursor supports MCP, meaning you can add custom tool servers quickly. Langbus documents MCP server -And make them accessible as AI-Augmented tools when you code.
Think about the cursor because the VS code meets AI agents-with built-in support for smart tools such as documents, such as document retrospectors and recover from code examples.
What is the Lang Base and why is its documents MCP server useful?
Langbus A powerful server lace AI platform for building AI agents with memory. This helps developers to create AI -powered apps and assistants by connecting their data, APIs and documents directly to LLMS.
Langbus documents MCP server The Lang Base provides access to documents and API reference. This server allows you to use the Lang Base documents as a context of your LLMS.
By connecting this server with a cursor (or any MCP -backed IDE), you can make Langbase Documents available to your AI agents according to demand. This means that when building server -lasted agents applications, it means switching to less context, faster workflows, and smart assistance.
How to configure Lang Base Documents MCP Server in Cursor
Let’s go through step by step to the server.
1. The cursor’s open settings
Launch cursor and open settings. From the left sidebar, select the MCP.
2. Add a new MCP server
Click on yellow + Add the new Global MCP server button.
3. Lang Base Documents Create MCP Server
Paste the following sequence mcp.json File:
{
"mcpServers": {
"Langbase": {
"command": "npx",
"args": ("@langbase/cli","docs-mcp-server")
}
}
}
4. Langbes documents start MCP server
In your terminal, drive:
pnpm add @langbase/cli
And then run this command:
pnpm dlx @langbase/cli docs-mcp-server
5. Enable the MCP server in the cursor
In MCP settings, make sure the Lang Base Server has been tugged to activate.
How to use Langbase Documents MCP Server in Cursor AI
Once everything ends, the cursor’s AI agent can now call Langbus document tools like:
For example, you can ask the cursor agent:
“Show me the API reference for Langbase Memory”
or
“Find a code example of creating an AI agent pipe in Langbase”
The document will use the MCP server to bring the exact pieces of AI documents – directly inside the cursor.
Use case: Langbus documents Create AII Agent with MCP Server
Let’s create a summary agent that offers a summary of the context using the Lang Base Documentation MCP Server using the Lang Base SD SD by the Cursor AI Code Editor.
Open the empty folder in the cursor and launch the chat panel (
Cmd+Shift+IOn the mac orCtrl+Shift+IOn Windows).Go from the mode selector to the agent and select your preferred LLM (we will use Claude 3.5 Sonit for this demo).
In the chat input, enter the following prompt:
“In this directory, make a summary pipe agent, using the Lang Base SD. Use type scripts and PNPMs to operate the agent in the terminal. “The cursor will automatically request MCP calls, produce the desired files and code that use langabis documents as context, and suggest changes. Accept the changes, and your summary agent will be ready. You can run the agent using the cursor commands and see the results.
Here is a demo video to make this summary agent with the same prompt and Lang Base documents MCP server.
https://www.youtube.com/watch?v=pw6su5hpwwu
By lining the MCP server with the cursor AI, you have learned how to prepare the server -equipped AI agents in minutes – all of this except your IDE.
If you are creating apps with AI agents, tools, or Lang Base, this is one of the fastest ways to simplify your development process.
Happy building! 🚀 🚀
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