How to Use the Unstructured MCP in LlamaIndex

Q: How do I find out what kind of data sources Unstructured supports when using LlamaIndex?

You use listdatasources to retrieve a searchable index of all configured remote connectors. This output becomes part of your knowledge base, allowing semantic search against connectivity details.

Q: If my workflow fails, how can I check its history with Unstructured and LlamaIndex?

The listworkflowjobs tool provides historical execution data. By indexing this output, your RAG application grounds answers in verifiable job status reports, preventing hallucinations.

Q: What kind of unstructured metadata can I query using Unstructured and LlamaIndex?

You index the results from getworkflowdetails to make configuration parameters searchable. This means users can ask complex questions about specific workflow setup details and get an answer grounded in API data.

Q: Can I list all possible processing pipelines with Unstructured and LlamaIndex?

Yes. The listprocessingworkflows tool gives a full listing of every end-to-end document processing pipeline. Indexing this ensures that the entire catalogue is available for semantic search.

Index Unstructured Pipelines: Build knowledge-augmented RAG applications with LlamaIndex.

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Connect Unstructured MCP to LlamaIndex

Create your Vinkius account to connect Unstructured 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 Unstructured with LlamaIndex

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Index Workflow Configuration via MCP Server

LlamaIndex can index the operational metadata of your data sources. By calling `list_data_sources`, you capture a searchable record of all remote connectors (S3, GCS). This allows RAG to answer questions like, 'Which cloud storage locations do we connect to?' Similarly, indexing results from `get_workflow_details` lets the agent retrieve specific configuration parameters. Instead of hallucinating about setup steps, LlamaIndex grounds answers in verifiable API data.

Track Job Status with MCP Server for LlamaIndex

When you need to query past transformations, `list_workflow_jobs` provides the necessary historical context. The agent indexes this output, so users can ask questions like, 'What was the status of the job run yesterday?' This is crucial for building robust RAG pipelines. It transforms ephemeral operational data into persistent knowledge that your LlamaIndex application can search against.

List and Compare Pipelines Using MCP Server

The client uses `list_processing_workflows` to index every available document processing pipeline. This capability lets the developer build a comprehensive, queryable knowledge base of all possible data transformation paths. Furthermore, comparing potential destinations is simple: `list_data_destinations` provides a list of target locations (Vector DBs, SQL) that can be indexed and referenced when answering user queries.

Setup guide

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

You use `list_data_sources` to retrieve a searchable index of all configured remote connectors. This output becomes part of your knowledge base, allowing semantic search against connectivity details.

The `list_workflow_jobs` tool provides historical execution data. By indexing this output, your RAG application grounds answers in verifiable job status reports, preventing hallucinations.

You index the results from `get_workflow_details` to make configuration parameters searchable. This means users can ask complex questions about specific workflow setup details and get an answer grounded in API data.

Yes. The `list_processing_workflows` tool gives a full listing of every end-to-end document processing pipeline. Indexing this ensures that the entire catalogue is available for semantic search.

This server handles metadata and configuration definitions related to unstructured data sources, including connector endpoints and workflow details. The indexed knowledge base focuses on these structural elements.

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