Mastra Docs Chatbot

What is Mastra Docs Chatbot?

Mastra Docs Chatbot is a Next.js application that uses Mastra agents with Model Context Protocol (MCP) integration to provide a chat interface for answering questions about Mastra documentation, code examples, and best practices. It is designed for developers and users of Mastra who need quick, accurate documentation assistance.

How to use Mastra Docs Chatbot?

Clone the repository, install dependencies with npm install or pnpm install, configure a .env.local file with your OpenAI API key, then run npm run dev or pnpm dev and open http://localhost:3000. The homepage features the Docs Agent, which answers questions about Mastra documentation and maintains conversation history across sessions.

Key features of Mastra Docs Chatbot

• Specialized Mastra agent with custom model and tools
• Model Context Protocol (MCP) integration for documentation search
• Real-time streaming responses
• Conversation memory between sessions
• Modern UI built with Assistant UI components

Use cases of Mastra Docs Chatbot

• Answering questions about Mastra documentation and concepts
• Providing code examples and best practices for Mastra
• Searching through Mastra examples, blog posts, and changelog information
• Maintaining conversational context across multiple sessions

FAQ from Mastra Docs Chatbot

What is the Docs Agent?

The Docs Agent is a Mastra agent that uses the Mastra MCP docs server to provide accurate information about Mastra documentation, code examples, and best practices.

What are the prerequisites for running the app?

You need Node.js 18 or later, npm or pnpm, and an OpenAI API key to configure in a .env.local file.

How does the app access documentation?

It uses Mastra agents with Model Context Protocol (MCP) integration to search through Mastra documentation, examples, blog posts, and package changelog information.

Does the app require an internet connection?

Yes, it requires an OpenAI API key and accesses external documentation via MCP, so an internet connection is needed during use.

What transport or authentication is used?

Authentication is handled via the OpenAI API key set in the environment variable OPENAI_API_KEY. The transport is standard HTTP/HTTPS for API calls and MCP integration.