How to Use the HighRadius MCP in LangChain

Q: How does LangChain handle HighRadius pagination for invoices?

Your agent calls listinvoices and processes the returned batch. You write a loop in your LangGraph setup to handle subsequent pages if the outstanding receivable list is long.

Build ReAct agents in LangChain that automatically reconcile HighRadius payments and track cash application status.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect HighRadius MCP to LangChain

Create your Vinkius account to connect HighRadius to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup HighRadius with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automated cash application pipelines

LangChain agents excel at multi-step reasoning. You plug this MCP Server into your graph, and suddenly your agent can pull `list_payments` and map them against `list_invoices` without human intervention. The real capability comes from chaining these operations. An agent grabs the output from `get_payment`, formats the IDs, and feeds them directly into `reconcile_payment` to clear the ledger.

Dispute management routing

Short pays require context. A LangChain agent can detect partial payments via `get_cash_application_status` and immediately trigger a secondary chain to investigate. If the numbers fail to match, the agent runs `create_dispute` using the exact invoice ID and variance amount. You track every step of this decision tree in LangSmith.

Customer context aggregation

Financial operations rarely happen in a vacuum. Your agent pulls `get_customer` data and combines it with internal CRM records in the same chain. It then runs `list_deductions` to spot historical patterns for that specific account. You get a complete view of the buyer's payment behavior before the agent decides whether to write off the variance or fight it.

Setup guide

Set up HighRadius MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes HighRadius tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "highradius-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent HighRadius transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HighRadius. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect HighRadius now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about HighRadius MCP in LangChain

Use MultiServerMCPClient and point it to your server URL. Call client.get_tools() to expose the HighRadius endpoints directly to your ReAct agent.

Yes. You configure a chain to evaluate payment variances. If the rules match, the agent executes create_dispute with the required invoice ID, reason, and amount.

Your agent calls list_invoices and processes the returned batch. You write a loop in your LangGraph setup to handle subsequent pages if the outstanding receivable list is long.

The MCP protocol passes the error back to the agent. You set up fallback logic in LangChain to alert a human or retry the match with different invoice IDs.

The server accesses sensitive invoice totals, payment IDs, and customer deduction histories. Vinkius isolates this execution in a V8 sandbox, ensuring your LangChain environment reads the data ephemerally without storing it.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript