LlamaCloud MCP. Manage document parsing and RAG pipelines conversationally.

You'll connect your AI agent right into the thick of your company's data infrastructure. This LlamaCloud server lets you take full control of everything from setting up complex Retrieval-Augmented Generation (RAG) pipelines to parsing messy, multi-page documents—and you do it all just by talking to your agent. You don't gotta write a line of Python code for this.

You can manage the entire document lifecycle and audit your data indices right through conversation. This means your agent handles everything from raw files into perfectly structured context that your LLM needs to answer questions accurately. It’s about keeping your AI grounded in your actual corporate knowledge, not some generic internet garbage.

When you're setting up or auditing your system, you can start by getting a bird's-eye view of what's running. You use the list_projects tool to pull up a list of every high-level container—the managed LlamaCloud projects—that hold groups of related pipelines and indices. Once you know which project holds the data you need, you run list_pipelines to get an inventory of every single data pipeline running across your account.

If you wanna deep dive into just one specific setup, you use get_pipeline, passing in a name, and it pulls back the full configuration details—that's where you see exactly which sources (like S3 buckets or Google Drive folders) are feeding data and how the indices are set up.

For the document parsing side of things, this is killer. Instead of having to manually copy-paste from a PDF, you use create_parsing_upload to send an explicit file—say, last year's annual report or a technical manual—straight to LlamaParse. This tool doesn't just read text; it figures out the layout, converting tables, complex sections, and even some handwriting into clean, structured Markdown context.

Once that job is submitted, you can’t just assume it worked. You run list_parsing_jobs to check all active or recently finished parsing jobs so you know where your document ingestion stands. When the processing finishes, you use get_parsing_result, providing the specific job ID, and it spits out the final, structured Markdown text output that’s ready for your agent to query.

This setup gives your agent full command over data flow: list projects, audit pipelines, check specific configurations, manage document uploads, track parsing status, and grab clean results. You're managing a sophisticated RAG system—from raw file upload through structured index creation—all conversational commands away.