How to Use the Captain Data MCP in LangChain

Build complex lead enrichment chains in LangChain using Captain Data to feed your agents real-time firmographic intelligence.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Captain Data MCP to LangChain

Create your Vinkius account to connect Captain Data 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 Captain Data with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain lead discovery with LangChain

Use `search_companies` or `search_people` to feed your chain's initial context. Your agent processes these results to refine subsequent queries. Pass the output directly into `enrich_company` or `enrich_person` to verify lead details before finalizing your CRM updates.

Trace every MCP Server decision

Monitor every Captain Data tool call through LangSmith. You see exactly what data hits the chain and how the agent reacts. Latency and token counts appear in real-time. This helps you debug why an agent picked one lead over another.

Automate multi-step research flows

Create a loop where `list_workflows` checks status and `get_job_details` triggers on completion. The agent handles the handoff automatically. This MCP Server provides the raw data your chain needs to make decisions without manual input.

Setup guide

Set up Captain Data 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 Captain Data 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({
    "captain-data-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 Captain Data 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 Captain Data. 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 Captain Data 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 Captain Data MCP in LangChain

Install the MCP adapters and point the client to your endpoint. You simply pass the tools into your agent definition to start executing logic.

Yes, you can build a pipeline that searches for companies and enriches them in one go. It removes the need for manual data entry.

Absolutely. You can aggregate multiple sources into a single chain context. The agent decides which tool to call based on the current step.

Use LangSmith tracing to view every request. It captures the exact JSON input sent to the server and the response returned.

Your firmographic data stays within your execution environment. We use ephemeral sessions that clear after your LangChain agent finishes its task.

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

Mastra AI sdk-typescript

Start using the Captain Data MCP today

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