A Model Context Protocol (MCP) server that provides AI-powered search and querying capabilities for the Vercel AI SDK documentation. This project enables developers to ask questions about the Vercel AI SDK and receive accurate, contextualized responses based on the official documentation.
A Model Context Protocol (MCP) server that provides AI-powered search and querying capabilities for the Vercel AI SDK documentation. This project enables developers to ask questions about the Vercel AI SDK and receive accurate, contextualized responses based on the official documentation.
This system consists of several key components:
Create a .env
file in the project root with the following variables:
GOOGLE_GENERATIVE_AI_API_KEY=your-google-api-key-here
You'll need to obtain a Google Gemini API key from the Google AI Studio.
Clone the repository
git clone https://github.com/IvanAmador/vercel-ai-docs-mcp.git
cd vercel-ai-docs-mcp-agent
Install dependencies
npm install
Build the project
npm run build
Build the documentation index
npm run build:index
Start the MCP server
npm run start
Claude Desktop is a powerful AI assistant that supports MCP servers. To connect the Vercel AI SDK Documentation MCP agent with Claude Desktop:
First, install Claude Desktop if you don't have it already.
Open Claude Desktop settings (via the application menu, not within the chat interface).
Navigate to the "Developer" tab and click "Edit Config".
Add the Vercel AI Docs MCP server to your configuration:
{
"mcpServers": {
"vercel-ai-docs": {
"command": "node",
"args": ["ABSOLUTE_PATH_TO_PROJECT/dist/main.js"],
"env": {
"GOOGLE_GENERATIVE_AI_API_KEY": "your-google-api-key-here"
}
}
}
}
Make sure to replace:
ABSOLUTE_PATH_TO_PROJECT
with the actual path to your project folderyour-google-api-key-here
with your Google Gemini API keySave the config file and restart Claude Desktop.
To verify the server is connected, look for the hammer 🔨 icon in the Claude chat interface.
For more detailed information about setting up MCP servers with Claude Desktop, visit the MCP Quickstart Guide.
This MCP server is compatible with any client that implements the Model Context Protocol. Here are a few examples:
Cursor is an AI-powered code editor that supports MCP servers. To integrate with Cursor:
Add a .cursor/mcp.json
file to your project directory (for project-specific configuration) or a ~/.cursor/mcp.json
file in your home directory (for global configuration).
Add the following to your configuration file:
{
"mcpServers": {
"vercel-ai-docs": {
"command": "node",
"args": ["ABSOLUTE_PATH_TO_PROJECT/dist/main.js"],
"env": {
"GOOGLE_GENERATIVE_AI_API_KEY": "your-google-api-key-here"
}
}
}
}
For more information about using MCP with Cursor, refer to the Cursor MCP documentation.
The MCP server exposes three primary tools:
Query the Vercel AI SDK documentation using an AI agent that can search and synthesize information.
{
"name": "agent-query",
"arguments": {
"query": "How do I use the streamText function?",
"sessionId": "unique-session-id"
}
}
Perform a direct similarity search against the Vercel AI SDK documentation index.
{
"name": "direct-query",
"arguments": {
"query": "streamText usage",
"limit": 5
}
}
Clears the conversation memory for a specific session or all sessions.
{
"name": "clear-memory",
"arguments": {
"sessionId": "unique-session-id"
}
}
To clear all sessions, omit the sessionId parameter.
├── config/ # Configuration settings
├── core/ # Core functionality
│ ├── indexing/ # Document indexing and vector store
│ └── query/ # Query services (agent and direct)
├── files/ # Storage directories
│ ├── docs/ # Processed documentation
│ ├── faiss_index/ # Vector index files
│ └── sessions/ # Session data
├── mcp/ # MCP server and tools
│ ├── server.ts # MCP server implementation
│ └── tools/ # MCP tool definitions
├── scripts/ # Build and utility scripts
└── utils/ # Helper utilities
npm run build
: Compile TypeScript filesnpm run build:index
: Build the documentation indexnpm run dev:index
: Build and index in development modenpm run dev
: Build and start in development modeIndex not found or failed to load
Run npm run build:index
to create the index before starting the server.
API rate limits
When exceeding Google API rate limits, the agent service may return errors. Implement appropriate backoff strategies.
Model connection issues
Ensure your Google API key is valid and has access to the specified Gemini model.
Claude Desktop not showing MCP server
Contributions are welcome! Please feel free to submit a Pull Request.
MIT