LlamaIndex (AI Data Framework & RAG) MCP Server for Mastra AI 6 tools — connect in under 2 minutes
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect LlamaIndex (AI Data Framework & RAG) through the Vinkius and Mastra agents discover all tools automatically — type-safe, streaming-ready, and deployable anywhere Node.js runs.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token — get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"llamaindex-ai-data-framework-rag": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "LlamaIndex (AI Data Framework & RAG) Agent",
instructions:
"You help users interact with LlamaIndex (AI Data Framework & RAG) " +
"using 6 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with LlamaIndex (AI Data Framework & RAG)?"
);
console.log(result.text);
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About LlamaIndex (AI Data Framework & RAG) MCP Server
Connect your LlamaIndex (LlamaCloud) account to any AI agent and take full control of your RAG data framework and semantic search orchestration through natural conversation.
Mastra's agent abstraction provides a clean separation between LLM logic and LlamaIndex (AI Data Framework & RAG) tool infrastructure. Connect 6 tools through the Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution — deployable to any Node.js host in one command.
What you can do
- RAG Orchestration — Execute structural natural language queries directly against your data pipelines to retrieve synthesized answers grounded in your source documents
- Index Visibility — List managed active indices wrapping your semantic stores and verify how your data is distributed across indexed databases
- File Audit — Retrieve explicit metadata for raw source files currently ingested by your pipelines to verify document tracking and ingestion limits
- Pipeline Management — List deployed data pipelines and retrieve detailed configurations including connected sources and embedding settings directly from your agent
- Project CRM — Navigate across high-level LlamaIndex projects managing collections of pipelines and queryable semantic search boundaries securely
- Real-time Synthesis — Use your agent to perform real-time RAG extraction, ensuring your AI workflows are powered by accurate, indexed enterprise knowledge
The LlamaIndex (AI Data Framework & RAG) MCP Server exposes 6 tools through the Vinkius. Connect it to Mastra AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect LlamaIndex (AI Data Framework & RAG) to Mastra AI via MCP
Follow these steps to integrate the LlamaIndex (AI Data Framework & RAG) MCP Server with Mastra AI.
Install dependencies
Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.ts and run with npx tsx agent.ts
Explore tools
Mastra discovers 6 tools from LlamaIndex (AI Data Framework & RAG) via MCP
Why Use Mastra AI with the LlamaIndex (AI Data Framework & RAG) MCP Server
Mastra AI provides unique advantages when paired with LlamaIndex (AI Data Framework & RAG) through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure — add LlamaIndex (AI Data Framework & RAG) without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every LlamaIndex (AI Data Framework & RAG) tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host — Vercel, Railway, Fly.io, or your own infrastructure
LlamaIndex (AI Data Framework & RAG) + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the LlamaIndex (AI Data Framework & RAG) MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query LlamaIndex (AI Data Framework & RAG), process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed LlamaIndex (AI Data Framework & RAG) as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query LlamaIndex (AI Data Framework & RAG) on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using LlamaIndex (AI Data Framework & RAG) tools alongside other MCP servers
LlamaIndex (AI Data Framework & RAG) MCP Tools for Mastra AI (6)
These 6 tools become available when you connect LlamaIndex (AI Data Framework & RAG) to Mastra AI via MCP:
get_pipeline
Get configuration details for a specific pipeline
list_files
List raw source files currently ingested by a pipeline
list_indexes
List LlamaCloud active indexes
list_pipelines
List LlamaCloud deployed data pipelines
list_projects
List active LlamaCloud projects
query_pipeline
Execute a natural language query against a specific Pipeline
Example Prompts for LlamaIndex (AI Data Framework & RAG) in Mastra AI
Ready-to-use prompts you can give your Mastra AI agent to start working with LlamaIndex (AI Data Framework & RAG) immediately.
"Query the 'Product-Docs' pipeline about 'multi-tenant security architecture'"
"List all files ingested by the 'Engineering-Handbook' pipeline (ID: pipe-123)"
"What are the active LlamaCloud projects in our organization?"
Troubleshooting LlamaIndex (AI Data Framework & RAG) MCP Server with Mastra AI
Common issues when connecting LlamaIndex (AI Data Framework & RAG) to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpLlamaIndex (AI Data Framework & RAG) + Mastra AI FAQ
Common questions about integrating LlamaIndex (AI Data Framework & RAG) MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
Connect LlamaIndex (AI Data Framework & RAG) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
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Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect LlamaIndex (AI Data Framework & RAG) to Mastra AI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.