MCP For Claude Marketplace
LlamaCloud (Managed RAG & Parsing)

LlamaCloud (Managed RAG & Parsing) MCP Connector for Claude

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Manage RAG pipelines and document parsing via LlamaCloud — orchestrate LlamaParse jobs and audit data ingestion.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner
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Connect your LlamaCloud account to any AI agent and take full control of your enterprise RAG infrastructure and AI-powered document parsing through natural conversation.

What you can do

  • Pipeline Orchestration — List all deployed data pipelines and retrieve detailed configurations including connected sources and index settings directly from your agent
  • AI Document Parsing — Dispatch complex files (PDFs, docs) to LlamaParse to convert intricate layouts, tables, and handwriting into structured Markdown context
  • Job Monitoring — Track the status of ongoing parsing jobs and retrieve extraction results once processing is complete to power your AI workflows
  • Project Management — Navigate high-level LlamaCloud projects managing collections of pipelines and queryable indices securely
  • Unstructured Data Ingestion — Monitor the flow of raw data into your managed indices and verify processing states for high-quality LLM grounding
  • Diagnostic Audit — Fetch final parsed outputs and job traces to ensure data integrity and layout accuracy across your RAG pipeline

How it works

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

Who is this for?

  • RAG Developers — automate the ingestion of complex enterprise documents and monitor pipeline health through natural conversation
  • AI Engineers — verify document parsing quality and orchestrate large-scale data extraction jobs without manual Python scripts
  • Data Scientists — audit managed indices and track parsing statuses to ensure high-quality fact-grounding for AI agents
ragdocument-parsingdata-ingestionpipeline-orchestrationvector-indices

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_projects

List active LlamaCloud projects

list_parsing_jobs

List LlamaParse active parsing jobs tracking document ingestion

create_parsing_upload

Dispatch a file explicitly to LlamaParse

get_parsing_result

Retrieve the final markdown/rich-text extraction from LlamaParse

See how to talk to your AI agent using LlamaCloud (Managed RAG & Parsing).

List all active data pipelines in my LlamaCloud account

I've found 3 active pipelines: 'Financial-Reports-Index' (ID: pipe-123), 'Technical-Docs-RAG' (ID: pipe-456), and 'Customer-Support-KB' (ID: pipe-789). Which one would you like to check the source configuration for?

Parse this PDF file using LlamaParse: 'annual_report_2024.pdf'

File submitted to LlamaParse. Job ID: 'job-98765'. I'm monitoring the extraction process. LlamaCloud is currently processing complex tables and charts within the report. I'll provide the Markdown result as soon as it's ready.

Show me the configuration for the 'Technical-Docs-RAG' pipeline

Pipeline 'Technical-Docs-RAG' (ID: pipe-456) is configured with 2 sources: a S3 bucket ('s3://docs-bucket') and a Google Drive folder. It uses OpenAI 'text-embedding-3-small' for indexing and is mapped to your 'production-index' in LlamaCloud.

Absolutely. LlamaParse uses AI-driven parsing to turn complex PDF layouts, nested tables, and even handwriting into structured Markdown. Use the `create_parsing_upload` tool to start the process and retrieve high-quality context for your agent.

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