Affinda MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Affinda 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 Affinda. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in Affinda?"
)
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 Affinda MCP Server
Connect your Affinda account to your AI agent to unlock powerful intelligent document processing (IDP). From automatically extracting details from resumes and invoices to auditing document statuses across your workspaces, your agent handles structured data extraction through natural conversation.
LlamaIndex agents combine Affinda tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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
- Automated Document Parsing — Upload PDFs or images of resumes, invoices, and passports for high-accuracy JSON extraction
- Workspace Oversight — List and audit documents within your specific workspaces to maintain organizational control
- Extraction Model Management — List available document types (Resume, Invoice, Receipt, etc.) supported by your account
- Real-time Status Tracking — Retrieve the parsing status and technical metadata for any uploaded document
- Metadata Insights — Quickly identify processing errors or missing data across your document library directly from chat
The Affinda MCP Server exposes 5 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 Affinda to LlamaIndex via MCP
Follow these steps to integrate the Affinda 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 5 tools from Affinda
Why Use LlamaIndex with the Affinda MCP Server
LlamaIndex provides unique advantages when paired with Affinda through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Affinda tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Affinda tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Affinda, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Affinda tools were called, what data was returned, and how it influenced the final answer
Affinda + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Affinda MCP Server delivers measurable value.
Hybrid search: combine Affinda real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Affinda 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 Affinda for fresh data
Analytical workflows: chain Affinda queries with LlamaIndex's data connectors to build multi-source analytical reports
Affinda MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect Affinda to LlamaIndex via MCP:
create_document
Defaults to synchronous waiting for the output. Upload and parse a PDF or image into Affinda via its public URL for high-accuracy JSON extraction
get_document
Retrieve the fully structured JSON data and status for a specific processed document in Affinda
list_document_types
Retrieve exactly which parsing models the Affinda account supports (e.g. Resume, Invoice, Passport)
list_documents
Retrieve all parsed documents in an Affinda workspace with their processing status
list_workspaces
Retrieve all container workspaces for documents created within your Affinda account
Example Prompts for Affinda in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Affinda immediately.
"List all documents in my 'HR Recruitment' workspace."
"Parse this resume URL: https://example.com/cv.pdf using the 'resume' model."
"List the available document types in my account."
Troubleshooting Affinda MCP Server with LlamaIndex
Common issues when connecting Affinda to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAffinda + LlamaIndex FAQ
Common questions about integrating Affinda 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 Affinda 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 Affinda to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.