How to Use the HighRadius MCP in LlamaIndex

Q: Can LlamaIndex query specific HighRadius disputes?

It can. Your agent calls getinvoice or listdisputes to fetch the raw records, then embeds that text into your index for immediate semantic querying.

Q: Can LlamaIndex check real-time HighRadius cash application status?

You configure a FunctionAgent with access to the getcashapplicationstatus tool. The agent queries the live endpoint instead of relying solely on stale vector data.

Q: Do I need to store the payment records permanently?

No. You use the MCP integration to fetch getpayment results on the fly during a conversation, keeping the data ephemeral.

Index HighRadius payment data and customer disputes directly into LlamaIndex to build financially-aware RAG applications.

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Connect HighRadius MCP to LlamaIndex

Create your Vinkius account to connect HighRadius 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 HighRadius with LlamaIndex

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Semantic search for financial records

Connecting this MCP Server to LlamaIndex turns your accounts receivable data into a queryable knowledge base. You run `list_invoices` and push the outstanding receivables straight into your vector store. When a user asks about a specific account, the system skips the guesswork. It retrieves the exact balances and due dates, grounding every answer in live ERP reality.

Indexing customer deduction histories

Trade promotions and short pays create massive data trails. Your LlamaIndex application can pull `list_deductions` and index every historical variance alongside your standard operating procedures. This means your agent queries past resolutions before handling a new issue. If it finds a match, it pulls `get_customer` to verify account standing before generating a response.

Context-aware payment reconciliation

Matching cash requires deep context. An agent uses `list_payments` to pull unapplied cash records and indexes them against open invoice text. Once the semantic engine finds the likely matches, it executes `reconcile_payment` with the exact payment ID and invoice arrays. You get automated matching backed by vector search logic.

Setup guide

Set up HighRadius 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 HighRadius 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 HighRadius tools.",
)
response = await agent.run("List recent HighRadius data")

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Common questions about HighRadius MCP in LlamaIndex

Install llama-index-tools-mcp and initialize BasicMCPClient. Pass the client to McpToolSpec to expose the HighRadius endpoints to your indexing pipeline.

It can. Your agent calls get_invoice or list_disputes to fetch the raw records, then embeds that text into your index for immediate semantic querying.

You configure a FunctionAgent with access to the get_cash_application_status tool. The agent queries the live endpoint instead of relying solely on stale vector data.

No. You use the MCP integration to fetch get_payment results on the fly during a conversation, keeping the data ephemeral.

Your vector store handles highly sensitive customer names, dispute amounts, and payment histories. The Vinkius zero-trust architecture ensures the MCP Server only authenticates via your single endpoint token, keeping the data transit secure.

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