How to Use the Carta MCP in LangChain

Q: Can my LangChain agent read Carta cap tables?

Yes. The agent calls getcaptable to pull the full ownership breakdown. It parses the JSON output to answer specific questions about founder or investor stakes.

Build equity-aware LangChain agents that query cap tables and parse funding rounds directly from Carta.

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Connect Carta MCP to LangChain

Create your Vinkius account to connect Carta 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 Carta with LangChain

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Traceable equity chains with LangChain MCP Server

LangChain ReAct agents need raw data to reason about ownership. You pass the Carta tools into your graph, and your agent starts answering complex equity questions. It pulls the latest 409A numbers via `list_valuations` and feeds that directly into a calculation chain. Every tool call logs straight to LangSmith. You see exactly how many tokens the agent burned while fetching `get_cap_table`. If a multi-step chain fails while parsing convertible notes, you catch the exact input that broke the `list_convertibles` call.

Chain stakeholder data into your workflows

Equity management rarely happens in a vacuum. Your LangChain setup takes the output from `list_stakeholders` and pipes it into your HR or directory tools. The agent figures out who holds what without you writing custom API glue code. You ask the agent to model a new funding round. It grabs the current structure using `list_share_classes`, checks the existing option pool with `list_option_plans`, and outputs a projected cap table. The entire reasoning process happens across connected chains.

Multi-server equity pipelines

You rarely just want Carta data. Using LangChain's MultiServerMCPClient, you combine this server with your internal database tools. The agent fetches company details via `get_company` and cross-references them against your CRM records in a single execution step. State management handles the context between calls. When your agent pulls `list_securities`, it remembers the specific share classes it found earlier. It builds a complete picture of your equity structure, step by step, deciding which tool to call next based on the live data.

Setup guide

Set up Carta 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 Carta 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({
    "carta-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 Carta 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 Carta. 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 Carta MCP in LangChain

Install langchain-mcp-adapters. Set up a MultiServerMCPClient pointing to the Vinkius endpoint, then pass client.get_tools() into your ReAct agent.

Yes. The agent calls `get_cap_table` to pull the full ownership breakdown. It parses the JSON output to answer specific questions about founder or investor stakes.

You get ReAct reasoning loops and LangSmith observability. The agent looks at `list_funding_rounds`, decides it needs more context, and automatically calls `list_convertibles` before answering you.

You configure retry logic within your LangGraph execution nodes. The MCP protocol handles the transport layer, while your chain manages the backoff strategy.

Vinkius runs the server in an ephemeral V8 Isolate Sandbox. Your employee names and equity grants pulled via `list_stakeholders` never touch our disk. The sandbox terminates immediately after the tool execution finishes.

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