Can I query my indexed documents using natural language through my agent?

Yes. Use the `query_pipeline` tool by providing the Pipeline ID and your natural language question. Your agent will trigger a real-time RAG extraction and return a synthesized answer based on the relevant source documents found in the index.

How do I check which files have been successfully ingested into a pipeline?

The `list_files` tool allows your agent to retrieve explicit metadata for all physical documents attached to a pipeline. This is perfect for auditing your data source boundaries and ensuring all required documents are correctly indexed.

Can my agent manage multiple semantic indices?

Absolutely. Use the `list_indexes` tool to see all active semantic stores managed by LlamaCloud. Your agent will report the index names and types, making it easy to identify the correct target for your search or ingestion workflows.

LlamaIndex (AI Data Framework & RAG) MCP Server for Cursor 6 tools — connect in under 2 minutes

Name: LlamaIndex (AI Data Framework & RAG)
Availability: InStock
Author: Vinkius

docs.cursor.com/context/model-context-protocol

Built by Vinkius GDPR 6 Tools IDE

Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

RecommendedModern Approach — Zero Configuration

Vinkius Desktop App

The modern way to manage MCP Servers — no config files, no terminal commands. Install LlamaIndex (AI Data Framework & RAG) and 2,500+ MCP Servers from a single visual interface.

Download Free Open SourceNo signup required

Classic Setup·json

{
  "mcpServers": {
    "llamaindex-ai-data-framework-rag": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Cursor's Agent mode turns LlamaIndex (AI Data Framework & RAG) into an in-editor superpower. Ask Cursor to generate code using live data from LlamaIndex (AI Data Framework & RAG) and it fetches, processes, and writes — all in a single agentic loop. 6 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.

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 Cursor 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 Cursor via MCP

Follow these steps to integrate the LlamaIndex (AI Data Framework & RAG) MCP Server with Cursor.

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"

Add the server config

Paste the JSON configuration above into the mcp.json file that opens

Save the file

Cursor will automatically detect the new MCP server

Start using LlamaIndex (AI Data Framework & RAG)

Open Agent mode in chat and ask: "Using LlamaIndex (AI Data Framework & RAG), help me..." — 6 tools available

Why Use Cursor with the LlamaIndex (AI Data Framework & RAG) MCP Server

Cursor AI Code Editor provides unique advantages when paired with LlamaIndex (AI Data Framework & RAG) through the Model Context Protocol.

Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context

Cursor's Composer feature can generate entire files using real-time data fetched through MCP — no copy-pasting from external dashboards

MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment

VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools

LlamaIndex (AI Data Framework & RAG) + Cursor Use Cases

Practical scenarios where Cursor combined with the LlamaIndex (AI Data Framework & RAG) MCP Server delivers measurable value.

Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP

Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically

Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates

Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data

LlamaIndex (AI Data Framework & RAG) MCP Tools for Cursor (6)

These 6 tools become available when you connect LlamaIndex (AI Data Framework & RAG) to Cursor 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 Cursor

Ready-to-use prompts you can give your Cursor 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 Cursor

Common issues when connecting LlamaIndex (AI Data Framework & RAG) to Cursor through the Vinkius, and how to resolve them.

Tools not appearing in Cursor

Ensure you are in Agent mode (not Ask mode). MCP tools only work in Agent mode.

Server shows as disconnected

Check Settings → Features → MCP and verify the server status. Try clicking the refresh button.

LlamaIndex (AI Data Framework & RAG) + Cursor FAQ

Common questions about integrating LlamaIndex (AI Data Framework & RAG) MCP Server with Cursor.

What is Agent mode and why does it matter for MCP?

Agent mode is Cursor's autonomous execution mode where the AI can perform multi-step tasks: reading files, editing code, running terminal commands, and calling MCP tools. Without Agent mode, Cursor operates in a simpler ask-and-answer mode that doesn't support tool calling. Always ensure you're in Agent mode when working with MCP servers.

Where does Cursor store MCP configuration?

Cursor looks for MCP server configurations in a mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.

Can Cursor use MCP tools in inline edits?

No. MCP tools are only available in Agent mode through the chat panel. Inline completions and Tab suggestions do not trigger MCP tool calls. This is by design — tool calls require user visibility and approval.

How do I verify MCP tools are loaded?

Open Settings → Features → MCP and look for your server name. A green indicator means the server is connected. You can also check Agent mode's available tools by clicking the tools dropdown in the chat panel.