Skip the Docs Hunt: AI-Powered Vercel SDK Answers in Your Assistant

Stop alt-tabbing between your AI assistant and the Vercel AI SDK documentation. This MCP server brings semantic search and contextual Q&A directly into Claude Desktop, Cursor, or any MCP-compatible client.

The Problem You Know Too Well

You're building with the Vercel AI SDK and need to understand how streamText works with custom parameters, or figure out the right way to handle tool calling. Instead of getting straight answers, you're:

• Opening browser tabs to search through docs
• Skimming through pages that almost answer your question
• Losing context when switching between your assistant and documentation
• Getting generic answers that miss SDK-specific nuances

What This MCP Server Actually Does

Semantic Documentation Search: Ask "How do I implement streaming with custom stop sequences?" and get precise answers with relevant code examples from the actual Vercel AI SDK docs.

Conversation Context: Follow up with "What about error handling in that scenario?" without re-explaining what you're working on.

Direct Integration: Works inside Claude Desktop, Cursor, or any MCP client - no context switching required.

Real Developer Scenarios

API Integration Questions: "How do I configure the OpenAI provider with custom headers?" - Get the exact configuration code instead of generic provider setup instructions.

Advanced Usage Patterns: "What's the difference between generateText and streamText for tool calling?" - Understand the specific trade-offs with concrete examples.

Troubleshooting: "Why is my streaming response cutting off early?" - Get targeted debugging steps based on common SDK issues.

Migration Help: "How do I upgrade from the old completion API to the new generateObject?" - See migration patterns with before/after code.

Technical Implementation

The server crawls and indexes the entire Vercel AI SDK documentation using FAISS for vector similarity search. It provides two query modes:

• Direct Search: Fast semantic search returning relevant documentation sections
• AI Agent: Google Gemini synthesizes answers from multiple doc sections with conversation memory

Three main tools via MCP:

• agent-query: Natural language Q&A with session context
• direct-query: Direct similarity search of the documentation
• clear-memory: Reset conversation context when switching topics

Setup: 5 Minutes to Running

git clone https://github.com/IvanAmador/vercel-ai-docs-mcp.git
cd vercel-ai-docs-mcp
npm install && npm run build
npm run build:index  # Creates the searchable documentation index

Add your Google API key to .env:

GOOGLE_GENERATIVE_AI_API_KEY=your-key-here

Claude Desktop Integration: Add to your MCP config:

{
  "mcpServers": {
    "vercel-ai-docs": {
      "command": "node",  
      "args": ["ABSOLUTE_PATH_TO_PROJECT/dist/main.js"],
      "env": {
        "GOOGLE_GENERATIVE_AI_API_KEY": "your-key-here"
      }
    }
  }
}

Cursor Integration: Add .cursor/mcp.json with the same configuration format.

Why This Belongs in Your Workflow

Instead of breaking flow to hunt through documentation, you ask questions in natural language and get SDK-specific answers with code examples. The conversation context means follow-up questions build on previous answers, making complex implementation discussions actually useful.

Whether you're prototyping with the AI SDK or debugging production issues, having documentation search integrated directly into your AI assistant eliminates the constant context switching that slows down development.

The MCP architecture means this works with any compatible client - Claude Desktop today, but also Cursor and other tools as MCP adoption grows.