How to Use the Fee Navigator MCP in LangChain

Q: Can LangChain handle complex audits using Fee Navigator?

Yes. You can create multi-step chains that call listaudits first, then drill down into specific records using getaudit. The agent decides the sequence based on the data it finds.

Use LangChain to chain merchant statement analysis, savings benchmarks, and proposal generation into automated reasoning pipelines.

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Connect Fee Navigator MCP to LangChain

Create your Vinkius account to connect Fee Navigator 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.

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Setup Fee Navigator with LangChain

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ChatGPT

Claude

Perplexity

Chain merchant statement analysis in LangChain

Trigger `upload_statement` and pipe the results directly into `analyze_statement` to identify hidden fees without manual intervention. Your agent parses the data and feeds findings into the next step of your chain. This creates a clear flow from raw PDF to actionable savings. You control the logic while the server handles the heavy lifting of merchant data extraction.

Contextualize merchant data with Fee Navigator

Feed outputs from `list_merchants` and `get_merchant` into your vector stores to keep your agent informed. Use `get_industry_stats` as a reference point to validate if the current processing rates are competitive. Your chain maintains state across these calls, ensuring the agent knows exactly which account it is auditing. This avoids redundant API calls and keeps your logic focused.

Automate proposal workflows with this MCP Server

Use `get_proposal` to retrieve granular savings data and format it for your downstream tasks. If the numbers don't add up, your agent can trigger `get_audit` to investigate the specific charges. This keeps your proposal generation cycle tight. You build the logic, and the server provides the verified financial data to back every claim.

Setup guide

Set up Fee Navigator 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 Fee Navigator 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({
    "fee-navigator-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 Fee Navigator transactions"
    })
    print(result["messages"][-1].content)

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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 Fee Navigator. 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 Fee Navigator MCP in LangChain

Install the required adapters and initialize the client using the provided URL. Once set up, call the tools directly within your LangGraph definition to start querying merchant data.

Yes. You can create multi-step chains that call `list_audits` first, then drill down into specific records using `get_audit`. The agent decides the sequence based on the data it finds.

The server handles all financial data through encrypted endpoints. Only the specific merchant statement data requested by your agent is processed, and it never leaves the secure session context.

LangSmith tracing captures every input and output for your tools. You can monitor exactly how many tokens your agent spends when it invokes `analyze_statement` or `get_industry_stats`.

Yes. By using the client session object, your agent retains context across multiple tool calls. This is essential for long-running audits that span several days.

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