LlamaIndex (AI Data Framework & RAG) Connector for AI agents.

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

Yes. Use the querypipeline 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.

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

The listfiles 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.

6 live capabilities

Query and manage LlamaCloud RAG pipelines via natural language.

Live agent request LlamaIndex (AI Data Framework & RAG) / Connector

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AI Agent

Why people use LlamaIndex (AI Data Framework & RAG)

LlamaIndex for Auditing RAG Data Pipelines

This Connector puts your entire LlamaCloud environment into your AI client. You can ask your agent to list the files in a pipeline, check the project structure, or query the data directly. You get a single interface to manage and interact with your RAG infrastructure.

What Vinkius changes

You get a natural language interface for your entire cloud data infrastructure.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

Real-world use case 01

Verifying file ingestion

A developer needs to know if a specific PDF was actually indexed.
Real-world use case 02

Checking embedding settings

An engineer wants to check the embedding settings for a new project.
Real-world use case 03

Auditing project boundaries

A data scientist needs to see how many projects are active.

Complete set · 6capabilities

The complete LlamaIndex (AI Data Framework & RAG) capability set.

These are the exact actions your AI can choose when you ask it to work with LlamaIndex (AI Data Framework & RAG).

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through LlamaIndex (AI Data Framework & RAG).

01 Capability
Get pipeline
Pull the specific configuration details for a single pipeline. This is useful for checking embedding settings or source connections.
02 Capability
List indexes
View all active indexes wrapping your semantic stores. Use this to see how your data is distributed across your database.
03 Capability
Query pipeline
Send a natural language query to a specific pipeline to get a grounded answer. This lets your agent act as a direct interface to your data.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through LlamaIndex (AI Data Framework & RAG).

04 Capability
List files
See the raw source files that a specific pipeline has already ingested. Use this to audit your document tracking and ingestion limits.
05 Capability
List projects
Browse through the high-level projects managing your collections of pipelines. This helps you navigate across different semantic search boundaries.
06 Capability
List pipelines
See all your deployed LlamaCloud data pipelines in one list. Use this to get a high-level view of your active data flows.

Set up in minutes

One URL. Then ask LlamaIndex (AI Data Framework & RAG) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LlamaIndex (AI Data Framework & RAG) from the conversation.

Choose your client

Live preview

Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_0oOSMctRD8JekU5V9qPIM95pb3koS0z8x0sprJvK/mcp

Step 01

Open Connectors

In Claude Web or Claude Desktop, open Settings and choose Connectors.
Step 02

Add the URL

Choose Add custom connector, name it LlamaIndex (AI Data Framework & RAG), and paste the URL above.
Step 03

Turn it on in chat

Select +, open Connectors, and enable LlamaIndex (AI Data Framework & RAG) for the conversation.

ChatGPT · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_0oOSMctRD8JekU5V9qPIM95pb3koS0z8x0sprJvK/mcp

Step 01

Open MCP settings

On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
Step 02

Add the URL

Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the LlamaIndex (AI Data Framework & RAG) URL.
Step 03

Save and start

Save the connection and enable LlamaIndex (AI Data Framework & RAG) in your conversation. Desktop may ask you to restart once.

Cursor · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llamaindex-ai-data-framework-rag": {
      "url": "https://edge.vinkius.com/vk_preview_0oOSMctRD8JekU5V9qPIM95pb3koS0z8x0sprJvK/mcp"
    }
  }
}

Step 01

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Step 02

Add the server config

Paste the JSON configuration above into the mcp.json file that opens
Step 03

Save the file

Cursor will automatically detect the new Connector
Step 04

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

VS Code Copilot · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llamaindex-ai-data-framework-rag": {
      "url": "https://edge.vinkius.com/vk_preview_0oOSMctRD8JekU5V9qPIM95pb3koS0z8x0sprJvK/mcp"
    }
  }
}

Step 01

Create MCP config

Create a .vscode/mcp.json file in your project root
Step 02

Add the server config

Paste the JSON configuration above
Step 03

Enable Agent mode

Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Step 04

Start using LlamaIndex (AI Data Framework & RAG)

Ask Copilot: "Using LlamaIndex (AI Data Framework & RAG), help me...". 6 tools available

Windsurf · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llamaindex-ai-data-framework-rag": {
      "url": "https://edge.vinkius.com/vk_preview_0oOSMctRD8JekU5V9qPIM95pb3koS0z8x0sprJvK/mcp"
    }
  }
}

Step 01

Open MCP Settings

Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
Step 02

Add the server

Paste the JSON configuration above into mcp_config.json
Step 03

Save and reload

Windsurf will detect the new server automatically
Step 04

Start using LlamaIndex (AI Data Framework & RAG)

Open Cascade and ask: "Using LlamaIndex (AI Data Framework & RAG), help me...". 6 tools available

Cline · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "llamaindex-ai-data-framework-rag": {
      "url": "https://edge.vinkius.com/vk_preview_0oOSMctRD8JekU5V9qPIM95pb3koS0z8x0sprJvK/mcp"
    }
  }
}

Step 01

Open Cline MCP Settings

Click the Connectors icon in the Cline sidebar panel
Step 02

Add remote server

Click "Add Connector" and paste the configuration above
Step 03

Enable the server

Toggle the server switch to ON
Step 04

Start using LlamaIndex (AI Data Framework & RAG)

Ask Cline: "Using LlamaIndex (AI Data Framework & RAG), help me...". 6 tools available

Claude Code · Terminal command

Official guide ↗

Advanced setup

claude mcp add llamaindex-ai-data-framework-rag --transport http "https://edge.vinkius.com/vk_preview_0oOSMctRD8JekU5V9qPIM95pb3koS0z8x0sprJvK/mcp"

Step 01

Install Claude Code

Run npm install -g @anthropic-ai/claude-code if not already installed
Step 02

Add the Connector

Run the command above in your terminal
Step 03

Verify the connection

Run claude mcp to list connected servers, or type /mcp inside a session
Step 04

Start using LlamaIndex (AI Data Framework & RAG)

Ask Claude: "Using LlamaIndex (AI Data Framework & RAG), show me...". 6 tools are ready

Where the request belongs

Work LlamaIndex can move forward.

Built around the request

This is for the RAG engineer who's tired of manual debugging and the data scientist who needs to audit complex semantic indexes without writing extra Python code.

RAG Developer

Tests semantic search relevancy by querying pipelines directly during development to save on manual testing time.

AI Engineer

Monitors document ingestion statuses and verifies metadata to ensure high-quality fact-grounding for production agents.

Data Scientist

Audits semantic index structures and manages data pipeline configurations across multiple enterprise projects efficiently.

Build the capability set

Each Connector adds new actions and data without changing how you work.

Browse Connectors

01 12 capabilities

GroundX

Data search and RAG optimization platform.

View Connector

02 6 capabilities

R2R

Equip your AI with direct access to your R2R engine. execute vector searches, run precise RAG queries, and manage your documents.

View Connector

03 7 capabilities

Haystack (deepset Cloud)

Build and manage AI-powered search and RAG pipelines via deepset Cloud. search documents, run pipelines, and manage workspaces.

View Connector

04 7 capabilities

Cognita (RAG Framework)

Manage modular RAG via Cognita. list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.

View Connector

05 6 capabilities

Elastic Enterprise Search

Manage enterprise search via Elastic. search engines and documents, handle indexing, and monitor search analytics directly from any AI agent.

View Connector

06 10 capabilities

Lucidworks Fusion (AI Search & Discovery)

Manage AI-powered search via Lucidworks Fusion. execute semantic queries, index documents, and monitor ML training jobs.

View Connector

Bring your own AI

Change the model, client or framework. Keep LlamaIndex connected.

Claude
ChatGPT
Gemini
Cursor
VS Code
Windsurf
ZCode
Cline
Zed
Continue
Kiro
Roo Code
Zencoder
Goose
Void
Augment Code
Amp
Qodo
Tabnine
Pieces
Sourcegraph Cody
JetBrains
Warp
Amazon Q
Antigravity
BoltAI
Raycast
Jan
LM Studio
AnythingLLM
Open WebUI
Msty
Cherry Studio
LibreChat
TypingMind
Chorus
5ire
n8n
LangChain
LlamaIndex
CrewAI
Vercel AI SDK

Before you connect

Questions about LlamaIndex.

The practical details behind the request, access and result.

What does the LlamaIndex MCP do for my RAG workflows?

It gives your AI agent a direct way to talk to your LlamaCloud data. You can query your pipelines, check your indices, and manage your projects using natural language instead of manual dashboard navigation.

Can I use the LlamaIndex MCP to query my LlamaCloud data?

Yes. You can ask your agent to perform real-time searches against specific pipelines to get answers grounded in your own enterprise documents.

How do I see which files are in my RAG pipeline with LlamaIndex MCP?

You can simply ask your agent to list the files for a specific pipeline. It will pull the metadata of the raw source files currently ingested in that flow.

Can this Connector help me manage multiple LlamaIndex projects?

Yes, it allows you to navigate across high-level projects and see which pipelines and indices are associated with each one, helping you stay organized.

Does the LlamaIndex MCP work with Claude or Cursor?

Yes, it works with any MCP-compatible client, including Claude, Cursor, and Windsurf, providing a consistent interface for your data management.

How do I check my pipeline configurations using LlamaIndex MCP?

You can ask your agent to show the configuration for any specific pipeline. It will retrieve details like embedding settings and connected sources for you.

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

Yes. Use the query_pipeline capability 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 capability 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 capability 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.

One connection away

Give your agent a direct line to LlamaIndex.

Connect LlamaIndex once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available

LlamaIndex for Auditing RAG Data Pipelines

You get a natural language interface for your entire cloud data infrastructure.

Verifying file ingestion

Checking embedding settings

Auditing project boundaries

Get pipeline

List indexes

Query pipeline

List files

List projects

List pipelines

Open Connectors

Add the URL

Turn it on in chat

Open MCP settings

Add the URL

Save and start

Open MCP Settings

Add the server config

Save the file

Start using LlamaIndex (AI Data Framework & RAG)

Create MCP config

Add the server config

Enable Agent mode

Start using LlamaIndex (AI Data Framework & RAG)

Open MCP Settings

Add the server

Save and reload

Start using LlamaIndex (AI Data Framework & RAG)

Open Cline MCP Settings

Add remote server

Enable the server

Start using LlamaIndex (AI Data Framework & RAG)

Install Claude Code

Add the Connector

Verify the connection

Start using LlamaIndex (AI Data Framework & RAG)

RAG Developer

AI Engineer

Data Scientist

GroundX

R2R

Haystack (deepset Cloud)

Cognita (RAG Framework)

Elastic Enterprise Search

Lucidworks Fusion (AI Search & Discovery)

Give your agent a direct line to LlamaIndex.