How to Use the Affinda MCP in LlamaIndex

Q: How do I filter documents in Affinda using LlamaIndex?

You can use listworkspaces to pick the right container and then pull documents from that specific location before indexing them.

Index your parsed documents directly into your LlamaIndex knowledge base.

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Connect Affinda MCP to LlamaIndex

Create your Vinkius account to connect Affinda 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 Affinda with LlamaIndex

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LlamaIndex document ingestion

Send images or PDFs to `create_document` and pipe the resulting JSON into a vector store. You stop wasting time on manual data entry and let the index build itself. This turns unstructured files into searchable knowledge. Your LlamaIndex application gains context from invoices or resumes that were previously locked in PDF format.

Retrieve structured document data

Call `get_document` to fetch the final JSON output for any file you have processed. The server returns the exact structure you need to populate your indices. This creates a clean data pipeline. You get high-accuracy extraction results that are ready for semantic search immediately.

Monitor your processing queue

Check the status of your uploads with `list_documents`. LlamaIndex can query this list to decide which files are ready for indexing. You keep your knowledge base fresh. If a document isn't ready, your agent skips it and checks back later, maintaining a consistent flow of information.

Setup guide

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

Absolutely. You grab the parsed JSON from the server and add it as a document node in your vector store.

It does. You use the McpToolSpec class to convert the server tools into a format that your LlamaIndex agents can execute.

You can use `list_workspaces` to pick the right container and then pull documents from that specific location before indexing them.

The status returned by the server will reflect the error. Your agent can read this status and alert you if a file needs re-uploading.

Your PDFs and images are processed in your secure Affinda workspace. Only the extracted text and metadata are transmitted to your local index.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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