How to Use the Extracta MCP in LlamaIndex

Q: Can I query historical Extracta runs using LlamaIndex?

Yes, you use getbatchresults to load past extractions into your vector database. This lets your LlamaIndex RAG pipeline search through older document data.

Q: How do I handle schema changes in LlamaIndex with Extracta?

You call updateextraction to adjust your target fields. LlamaIndex can then use viewextraction to dynamically align its indexing schema with the updated rules.

Q: Can the agent classify documents before indexing them?

Yes, the agent runs createclassification to determine the document type first. This lets you route invoices to one index and contracts to another.

Index structured document data extracted by Extracta directly into LlamaIndex using this MCP Server.

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Connect Extracta MCP to LlamaIndex

Create your Vinkius account to connect Extracta to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Extracta with LlamaIndex

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Index Extracta MCP Server outputs into LlamaIndex

The `get_results` tool retrieves structured JSON payloads from processed documents to feed your index. LlamaIndex takes this clean data and stores it as nodes, avoiding the noise of raw, unparsed text. This semantic indexing ensures your search queries return precise answers. You query actual database-like fields instead of guessing where the information lives in a messy PDF.

Build a searchable archive of past extractions

The `get_batch_results` tool pulls historical extraction data from your account to populate a local vector store. Your LlamaIndex agent queries this store to find patterns across hundreds of previous runs. This eliminates the need to re-process files when you need historical context. You search past extractions instantly using semantic queries grounded in real API outputs.

Verify extraction schemas before indexing

The `view_extraction` tool retrieves the active configuration rules for any given extraction ID. LlamaIndex reads this setup to understand exactly what keys and data types to expect in incoming documents. This pre-flight check prevents indexing errors caused by unexpected schema mismatches. Your vector store stays clean because the agent validates the structure before writing new nodes.

Setup guide

Set up Extracta 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 Extracta 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 Extracta tools.",
)
response = await agent.run("List recent Extracta 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 Extracta. 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 Extracta MCP in LlamaIndex

The MCP server retrieves structured JSON from your documents via `get_results`. LlamaIndex then converts these JSON key-value pairs into searchable vector nodes.

Yes, you use `get_batch_results` to load past extractions into your vector database. This lets your LlamaIndex RAG pipeline search through older document data.

You call `update_extraction` to adjust your target fields. LlamaIndex can then use `view_extraction` to dynamically align its indexing schema with the updated rules.

Yes, the agent runs `create_classification` to determine the document type first. This lets you route invoices to one index and contracts to another.

Your document payloads and extracted text strings are processed in memory and indexed directly into your local vector store. The server does not store your files permanently, keeping your private data confined to your secure storage.

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