Unstructured MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Unstructured as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Unstructured. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Unstructured?"
)
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 Unstructured MCP Server
Connect your Unstructured.io account to any AI agent to automate data ingestion and document processing pipelines seamlessly. Transform complex files into clean, AI-ready data without leaving your workflow.
LlamaIndex agents combine Unstructured tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Data Sources — List all configured remote data connectors (e.g. S3, GCS, SharePoint) to see where documents can be pulled from.
- Data Destinations — Browse target locations (like Vector DBs or SQL databases) where structured output is sent.
- Processing Workflows — List end-to-end pipelines, retrieve specific workflow configurations, and explore source-destination mappings.
- Job Execution — Manually trigger immediate document ingestion and partitioning jobs, and track their execution IDs.
- Job Monitoring — List active and historical workflow execution jobs to monitor the progress of your document processing tasks.
The Unstructured MCP Server exposes 6 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 Unstructured to LlamaIndex via MCP
Follow these steps to integrate the Unstructured 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 6 tools from Unstructured
Why Use LlamaIndex with the Unstructured MCP Server
LlamaIndex provides unique advantages when paired with Unstructured through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Unstructured tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Unstructured tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Unstructured, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Unstructured tools were called, what data was returned, and how it influenced the final answer
Unstructured + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Unstructured MCP Server delivers measurable value.
Hybrid search: combine Unstructured real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Unstructured 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 Unstructured for fresh data
Analytical workflows: chain Unstructured queries with LlamaIndex's data connectors to build multi-source analytical reports
Unstructured MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Unstructured to LlamaIndex via MCP:
get_workflow_details
Retrieves configuration details for a specific processing workflow
list_data_destinations
g. Vector DBs, SQL). Lists all configured target locations for processed data
list_data_sources
Lists all configured remote data connectors (e.g. S3, GCS)
list_processing_workflows
Lists all end-to-end document processing pipelines
list_workflow_jobs
Lists all active and historical workflow execution jobs
trigger_workflow_execution
Returns a job ID. Manually triggers an immediate execution of a processing workflow
Example Prompts for Unstructured in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Unstructured immediately.
"Show me all our active destination connectors."
"List the historical processing jobs from today."
"Trigger the engineering onboarding workflow."
Troubleshooting Unstructured MCP Server with LlamaIndex
Common issues when connecting Unstructured to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUnstructured + LlamaIndex FAQ
Common questions about integrating Unstructured 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 Unstructured 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 Unstructured to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.