How to Use the PDF.co MCP in LlamaIndex

Q: How do I convert scanned documents for LlamaIndex using PDF.co?

Your agent calls the ocrimage tool to extract raw text from the image. LlamaIndex then takes this text, creates document nodes, and indexes them into your vector database.

Q: Can I filter PDF.co metadata searches in LlamaIndex?

Yes, the extractpdfmeta tool gets file properties which are saved as metadata keys. You can run metadata filtering during LlamaIndex vector retrieval to narrow down your search.

Q: What is the setup for connecting the PDF.co MCP Server to LlamaIndex?

Install llama-index-tools-mcp and initialize the MCP client with your Vinkius HTTP URL. Convert the server tools into LlamaIndex tools using McpToolSpec and pass them to your agent.

Q: How do I handle multi-table documents with PDF.co and LlamaIndex?

It's simple. Use pdftocsv to turn tables into clean CSV data. Your LlamaIndex agent can then index these CSV rows as individual nodes, making tabular data easily queryable.

Index parsed PDF.co documents directly into LlamaIndex vector stores to build accurate RAG engines from complex files.

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Connect PDF.co MCP to LlamaIndex

Create your Vinkius account to connect PDF.co to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup PDF.co with LlamaIndex

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Structured data extraction for LlamaIndex RAG

The `pdf_to_json` tool converts complex layouts into structured objects that LlamaIndex parses into document nodes. This node structure preserves the hierarchy of tables and headings, preventing your vector database from losing context during chunks creation. Your pipeline then indexes these nodes directly into a vector store. When a user queries your RAG application, the engine searches this structured data to return precise answers based on the original document layout.

Building semantic search indexes with PDF.co MCP Server

The `pdf_to_text` tool extracts raw text from your files to feed the LlamaIndex document ingestion pipeline. Your agent runs this tool on demand via the MCP connection, converting raw reports into indexable text nodes without requiring local parsing libraries. For scanned documents, the agent executes `ocr_image` to extract text from images. The resulting text is indexed immediately, making scanned invoices and receipts searchable via standard semantic queries.

Dynamic document chunking and metadata extraction

The `extract_pdf_meta` tool gets document properties that LlamaIndex stores as node metadata. This metadata allows your search engine to filter query results by author, creation date, or custom tags before performing vector searches. When dealing with massive documents, the agent uses `split_pdf` to divide the file into chapters. Each chapter is parsed separately, ensuring that your vector index contains highly specific, bite-sized pieces of information.

Setup guide

Set up PDF.co MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all PDF.co MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to PDF.co tools.",
)
response = await agent.run("List recent PDF.co data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by PDF.co. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about PDF.co MCP in LlamaIndex

Your agent calls the `ocr_image` tool to extract raw text from the image. LlamaIndex then takes this text, creates document nodes, and indexes them into your vector database.

Yes, the `extract_pdf_meta` tool gets file properties which are saved as metadata keys. You can run metadata filtering during LlamaIndex vector retrieval to narrow down your search.

Install `llama-index-tools-mcp` and initialize the MCP client with your Vinkius HTTP URL. Convert the server tools into LlamaIndex tools using `McpToolSpec` and pass them to your agent.

It's simple. Use `pdf_to_csv` to turn tables into clean CSV data. Your LlamaIndex agent can then index these CSV rows as individual nodes, making tabular data easily queryable.

Your source images and PDF files are transmitted over HTTPS to the PDF.co API and are permanently deleted after the job completes. The Vinkius platform manages your API keys securely, so your agents never expose credentials in their chat logs.

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