Extracta MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Extracta as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Extracta. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Extracta?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Extracta MCP Server
Connect your Extracta.ai account to any AI agent and take full control of your automated data extraction and document classification through natural conversation.
LlamaIndex agents combine Extracta tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Extraction Orchestration — Create and configure new data extraction processes by defining JSON schemas for fields like dates, amounts, and item descriptions natively
- Live Document Processing — Submit publicly accessible file URLs (PDF, JPG, PNG) to trigger asynchronous extraction workflows and retrieve structured JSON data seamlessly
- AI Classification — Set up document classification rules to automatically sort documents into types like invoices, receipts, or contracts based on AI predictions
- Result Auditing — Retrieve extraction status and finalized structured data for specific documents, evaluating confidence scores and predicted categories flawlessly
- Batch History Monitoring — Fetch paginated lists of previously extracted documents and their associated data payloads to track historical processing limitlessly
- Configuration Mutation — Update existing extraction settings and mapping rules without creating new endpoints to refine your data parsing logic
- Workflow Management — View and manage extraction and classification configurations, including configured fields and webhook settings securely
The Extracta MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Extracta to LlamaIndex via MCP
Follow these steps to integrate the Extracta MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Extracta
Why Use LlamaIndex with the Extracta MCP Server
LlamaIndex provides unique advantages when paired with Extracta through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Extracta tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Extracta tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Extracta, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Extracta tools were called, what data was returned, and how it influenced the final answer
Extracta + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Extracta MCP Server delivers measurable value.
Hybrid search: combine Extracta real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Extracta to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Extracta for fresh data
Analytical workflows: chain Extracta queries with LlamaIndex's data connectors to build multi-source analytical reports
Extracta MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Extracta to LlamaIndex via MCP:
create_classification
g. invoice, receipt, contract). Pass JSON schema defining categories. Create a new Extracta document classification setup
create_extraction
g. language, format, expected fields like invoice_date, total_amount). Returns a new extractionId used for subsequent document processing. Create a new Extracta.ai data extraction process
delete_extraction
Subsequent uploads to this extractionId will fail. Delete an Extracta.ai extraction process
get_batch_results
Get bulk historical results from an Extraction process
get_classification_results
Get the predicted document category from Extracta
get_results
If not completed, it will indicate processing status. Get extraction results for a specific document
update_extraction
Modifies mapping rules without needing to create a new endpoint. Update an existing Extracta extraction configuration
upload_file_url
Returns a documentId. Use ea.get_results to poll for extracted data. Upload a document URL to Extracta for processing
view_classification
View details of an existing document classification process
view_extraction
View configuration of an existing Extracta extraction process
Example Prompts for Extracta in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Extracta immediately.
"Create an extraction process for invoices with fields: date, vendor, total"
"Extract data from this receipt URL: https://example.com/receipt.pdf"
"What type of document is doc_789 according to my classification rules?"
Troubleshooting Extracta MCP Server with LlamaIndex
Common issues when connecting Extracta to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpExtracta + LlamaIndex FAQ
Common questions about integrating Extracta MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Extracta with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Extracta to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.