MCP For Claude Marketplace
LlamaIndex (AI Data Framework & RAG)

LlamaIndex (AI Data Framework & RAG) MCP Connector for Claude

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Query and manage RAG pipelines via LlamaIndex — execute natural language searches, audit indexed files, and monitor data pipelines.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

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.

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

How it works

  1. Subscribe to this server
  2. Enter your LlamaCloud API Key
  3. Start querying your enterprise knowledge from Claude, Cursor, or any MCP-compatible client

Who is this for?

  • RAG Developers — test semantic search relevancy and query RAG pipelines through natural conversation without writing manual Python boilerplate
  • AI Engineers — monitor document ingestion statuses and verify indexed file metadata to ensure high-quality fact-grounding for AI agents
  • Data Scientists — audit semantic index structures and manage data pipeline configurations across multiple enterprise AI projects efficiently
ragsemantic-searchdata-frameworkunstructured-dataindexingllm-applications

6 tools expose this connector's capabilities to your AI agent.

list_pipelines

List LlamaCloud deployed data pipelines

get_pipeline

Get configuration details for a specific pipeline

list_indexes

List LlamaCloud active indexes

query_pipeline

Execute a natural language query against a specific Pipeline

list_files

List raw source files currently ingested by a pipeline

list_projects

List active LlamaCloud projects

See how to talk to your AI agent using LlamaIndex (AI Data Framework & RAG).

Query the 'Product-Docs' pipeline about 'multi-tenant security architecture'

Querying RAG pipeline… Based on your indexed documentation, the multi-tenant architecture uses isolated logical schemas per tenant and mandatory JWT-based attribute filtering at the gateway level. I've found 3 source documents explaining the row-level security implementation. Would you like the links?

List all files ingested by the 'Engineering-Handbook' pipeline (ID: pipe-123)

I've retrieved 15 files from the 'Engineering-Handbook' pipeline. Highlights include 'coding_standards.md', 'deployment_workflow.pdf', and 'api_best_practices.txt'. All files show a status of 'Ingested'. Would you like me to fetch the metadata for 'coding_standards.md'?

What are the active LlamaCloud projects in our organization?

I've identified 3 active LlamaCloud projects: 'Customer-Service-RAG' (ID: proj-001), 'Internal-Knowledge-Base' (ID: proj-005), and 'Market-Analysis-Tools' (ID: proj-008). Each project manages its own set of pipelines and indices. Which one would you like to explore?

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.

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