How to Use the Chatham Financial MCP in LangChain

Build complex financial reasoning pipelines in LangChain by chaining Chatham Financial market data and trade valuations.

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Connect Chatham Financial MCP to LangChain

Create your Vinkius account to connect Chatham Financial 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 Chatham Financial with LangChain

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Perplexity

Automated trade analysis in LangChain

Connect `get_trade_valuations` directly into your LangGraph chains to pull live pricing into your agent's reasoning loop. You can evaluate transaction health without manual data entry. Your agent inspects `get_trade_accounting` to build logic around hedge effectiveness. The output of one tool feeds the next, letting your chain decide if a position needs adjustment.

Market data integration for LangChain

Feed `get_chatham_market_data` into your LLM to ground your responses in real forward curves. It stops your agent from guessing interest rates. This MCP server gives your chain the exact benchmark rates required for accurate reporting. You get clean, structured data ready for immediate processing in your agent's next step.

Portfolio monitoring with LangChain

Use `list_chatham_portfolios` to give your agent a high-level view of all active positions. It maps out your entire financial footprint before running deeper analysis. Trigger `list_chatham_transactions` to isolate specific trade histories. Your agent parses these lists to identify trends or anomalies across your managed accounts.

Setup guide

Set up Chatham Financial 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 Chatham Financial 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({
    "chatham-financial-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 Chatham Financial 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 Chatham Financial. 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 Chatham Financial MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. You then pass the tool list directly into your agent constructor to enable access.

Yes, by calling the accounting tools during your chain execution. The agent receives the raw transaction records and formats them based on your specific logic.

We keep your financial data encrypted in transit via TLS. The server only handles the specific trade records you request and does not store them after the session ends.

It does. You can combine this server with other data sources in the same execution flow.

Your history stays within the source system. This MCP server acts as a pass-through that only transmits what you explicitly query.

Use it with your favorite AI tools

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

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