How to Use the iLEVEL (S&P Global) MCP in LangChain

Run multi-step private equity data pipelines in LangChain using direct iLEVEL (S&P Global) MCP tool calls.

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Connect iLEVEL (S&P Global) MCP to LangChain

Create your Vinkius account to connect iLEVEL (S&P Global) 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 iLEVEL (S&P Global) with LangChain

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Trace iLEVEL tool chains in LangSmith with this MCP Server

The `list_portfolios` and `list_investments` tools let you inspect every step of your private equity data extraction inside your LangChain agent. LangSmith tracks latency and token usage when your LangChain agent calls these iLEVEL tools. You see exactly how the LangChain agent decides to chain these iLEVEL queries together. If an iLEVEL query for a specific asset fails, you can isolate the error in your LangSmith tracing dashboard without digging through raw S&P Global logs.

Chain fund discovery to asset inspections in LangChain

The `list_funds` and `get_investment` tools let you feed the output of one iLEVEL call directly into the next within a LangChain graph. Your LangChain agent can run the fund discovery, filter them by criteria, and then invoke the asset inspection on the iLEVEL API. You do not write glue code to pass private equity variables between these LangChain steps. The LangChain framework handles the state transitions natively, letting the agent determine the execution path based on the iLEVEL JSON payloads.

Combine S&P Global data with external APIs

The `list_entities` tool maps legal structures to integrate your S&P Global private equity database with external APIs via LangChain. Your LangChain agent can query these iLEVEL structures, then pull external market indices to compare fund performance. The LangChain MultiServerMCPClient aggregates these iLEVEL tools into a single interface. Your LangChain workflow shifts from manual data stitching to automated cross-platform iLEVEL analysis in a single run.

Setup guide

Set up iLEVEL (S&P Global) 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 iLEVEL (S&P Global) 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({
    "ilevel-sp-global-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 iLEVEL (S&P Global) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about iLEVEL (S&P Global) MCP in LangChain

You provide your S&P Global API token as an environment variable when starting the Vinkius server. LangChain connects via the MultiServerMCPClient, which uses a single endpoint token to authorize all downstream calls to tools like `list_funds` and `list_users`.

Yes, every tool invocation from this MCP Server generates a detailed trace in LangSmith. You can monitor the exact input parameters sent by LangChain to `get_investment` and verify the latency of the S&P Global response.

You should implement exponential backoff within your LangChain runnable sequence. Because tools like `list_assets` can hit S&P Global rate limits during large runs, adding a retry wrapper prevents LangChain chain failures.

The langchain-mcp-adapters package automatically converts the server's schemas into LangChain tool formats. Your LangChain agent reads the exact parameters required by `list_contacts` or `list_entities` without manual schema mapping.

Vinkius runs the server inside a secure, ephemeral V8 Isolate sandbox that isolates your private equity data, including fund structures and asset valuations. No database records or user lists from `list_users` are ever stored on Vinkius servers; they pass directly to your local LangChain runtime.

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