LlamaCloud (Managed RAG & Parsing) Connector for AI agents.
6 live capabilities
Manage enterprise RAG pipelines and parse complex documents into structured data.
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Why people use LlamaCloud (Managed RAG & Parsing)
LlamaCloud (Managed RAG & Parsing) Solves Messy Enterprise Document Ingestion
With this Connector, you just tell your agent what to do. It handles the heavy lifting of turning those files into structured Markdown so your RAG system actually has high-quality data to work with. You get a conversational interface for your entire RAG backend.
What Vinkius changes
You get a conversational interface for managing complex RAG infrastructure and document parsing.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Parsing complex annual reports
A RAG developer needs to parse a 200-page PDF with complex tables and wants to use create_parsing_upload to get it into Markdown.
- Real-world use case 02
Auditing pipeline connections
An AI engineer wants to check if their Technical Docs pipeline is still connected to the right S3 bucket using get_pipeline.
- Real-world use case 03
Monitoring batch extractions
A data scientist wants to see if the latest batch of 500 documents finished processing by checking list_parsing_jobs.
Complete set · 6capabilities
The complete LlamaCloud (Managed RAG & Parsing) capability set.
These are the exact actions your AI can choose when you ask it to work with LlamaCloud (Managed RAG & Parsing).
01—03
3 capabilities in this set.
Part of 6 available through LlamaCloud (Managed RAG & Parsing).
- 01 Capability
Create parsing upload
Send a specific file to LlamaParse for conversion. This lets you start the extraction process for complex documents immediately.
- 02 Capability
List pipelines
See all your deployed LlamaCloud data pipelines. It helps you keep track of your different data sources at a glance.
- 03 Capability
Get pipeline
Look up the specific configuration for one pipeline. Use this to check source connections and index settings.
04—06
3 capabilities in this set.
Part of 6 available through LlamaCloud (Managed RAG & Parsing).
- 04 Capability
List projects
See all active LlamaCloud projects. This gives you a high-level view of your managed collections.
- 05 Capability
List parsing jobs
Track the status of active parsing tasks. Use this to monitor the progress of large batch extractions.
- 06 Capability
Get parsing result
Retrieve the final markdown output from a completed job. Use this to see exactly what your agent will use for grounding.
Set up in minutes
One URL. Then ask LlamaCloud (Managed RAG & Parsing) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LlamaCloud (Managed RAG & Parsing) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_AxNi4f2yJsdElAAsexdJyB2PpLLyipZfGMbLBQVd/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 LlamaCloud (Managed RAG & Parsing), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable LlamaCloud (Managed RAG & Parsing) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_AxNi4f2yJsdElAAsexdJyB2PpLLyipZfGMbLBQVd/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 LlamaCloud (Managed RAG & Parsing) URL.
- Step 03
Save and start
Save the connection and enable LlamaCloud (Managed RAG & Parsing) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"llamacloud-managed-rag-parsing": {
"url": "https://edge.vinkius.com/vk_preview_AxNi4f2yJsdElAAsexdJyB2PpLLyipZfGMbLBQVd/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 LlamaCloud (Managed RAG & Parsing)
Open Agent mode in chat and ask: "Using LlamaCloud (Managed RAG & Parsing), help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"llamacloud-managed-rag-parsing": {
"url": "https://edge.vinkius.com/vk_preview_AxNi4f2yJsdElAAsexdJyB2PpLLyipZfGMbLBQVd/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 LlamaCloud (Managed RAG & Parsing)
Ask Copilot: "Using LlamaCloud (Managed RAG & Parsing), help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"llamacloud-managed-rag-parsing": {
"url": "https://edge.vinkius.com/vk_preview_AxNi4f2yJsdElAAsexdJyB2PpLLyipZfGMbLBQVd/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 LlamaCloud (Managed RAG & Parsing)
Open Cascade and ask: "Using LlamaCloud (Managed RAG & Parsing), help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"llamacloud-managed-rag-parsing": {
"url": "https://edge.vinkius.com/vk_preview_AxNi4f2yJsdElAAsexdJyB2PpLLyipZfGMbLBQVd/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 LlamaCloud (Managed RAG & Parsing)
Ask Cline: "Using LlamaCloud (Managed RAG & Parsing), help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add llamacloud-managed-rag-parsing --transport http "https://edge.vinkius.com/vk_preview_AxNi4f2yJsdElAAsexdJyB2PpLLyipZfGMbLBQVd/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 LlamaCloud (Managed RAG & Parsing)
Ask Claude: "Using LlamaCloud (Managed RAG & Parsing), show me...". 6 tools are ready
Where the request belongs
Work LlamaCloud can move forward.
This is for the RAG developer who is tired of manual data cleaning or the AI engineer who needs to manage large-scale data extraction without writing custom scripts.
RAG Developer
You use this on a Tuesday to automate the ingestion of complex enterprise PDFs into your production indices.
AI Engineer
You use this to verify parsing quality and monitor the status of large batch document extractions.
Data Scientist
You use this to audit managed indices and ensure your AI agent has high-quality fact-grounding.
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Bring your own AI
Change the model, client or framework. Keep LlamaCloud connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
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Vercel AI SDK
Before you connect
Questions about LlamaCloud.
The practical details behind the request, access and result.
Can LlamaCloud (Managed RAG & Parsing) handle messy tables in PDFs?
Yes. It is specifically designed to handle complex layouts, including tables and multi-column text, by converting them into structured Markdown.
How do I use LlamaCloud (Managed RAG & Parsing) to manage my RAG pipelines?
You can use your AI agent to list all active pipelines, view specific configurations, and check the status of your data sources in one place.
Does LlamaCloud (Managed RAG & Parsing) support handwriting?
Yes, it can handle handwritten text within documents, converting it into clean text that your AI agent can easily process.
Can LlamaCloud (Managed RAG & Parsing) audit my data ingestion?
Absolutely. You can monitor raw data flows, check processing states, and verify that your managed indices are being updated correctly.
How does LlamaCloud (Managed RAG & Parsing) work with Claude?
It connects directly to Claude, allowing you to manage your entire RAG infrastructure through natural conversation instead of a dashboard.
Can LlamaCloud (Managed RAG & Parsing) parse multiple files at once?
Yes, you can monitor batch parsing jobs to track the progress of multiple documents being processed into your RAG system.
Can LlamaParse handle complex tables and layouts in my PDFs?
Absolutely. LlamaParse uses AI-driven parsing to turn complex PDF layouts, nested tables, and even handwriting into structured Markdown. Use the create_parsing_upload capability to start the process and retrieve high-quality context for your agent.
How do I check if my RAG data pipeline is finished processing?
Use the get_parsing_result capability with your specific Job ID. Your agent will poll the LlamaCloud API and report the current status. Once finished, it will retrieve the final parsed content ready for grounding.
Can I see all data sources connected to a specific pipeline?
Yes. The get_pipeline capability extracts the full configuration for any pipeline ID, identifying all connected data sources and configured index settings, ensuring you have a complete view of your ingestion flow.
One connection away
Give your agent a direct line to LlamaCloud.
Connect LlamaCloud once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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