How to Use the Datanyze MCP in LangChain

Q: Can LangChain track my API usage when querying Datanyze?

Yes, you can use getapicreditbalance inside your chain to monitor your remaining credits. LangSmith tracks every tool call, giving you full visibility into how many credits your agents consume during runs.

Run multi-step sales intelligence pipelines where your LangChain agent grabs Datanyze contact profiles and tracks software stacks.

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Connect Datanyze MCP to LangChain

Create your Vinkius account to connect Datanyze 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 Datanyze with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Trace Datanyze tech audits inside LangChain runs

`quick_tech_audit` lets your LangChain agent inspect any company's software stack to see what tools they run before pitching them. When your agent pulls this technographic data, LangSmith logs the exact Datanyze payload, latency, and token cost of your MCP Server runs so you can monitor the entire discovery pipeline. Your LangChain agent can then pipe those identified Datanyze technologies into subsequent chain steps. By chaining this profiling tool with your custom prompts, your LangChain agent makes real-time decisions about which sales play to execute based on active software footprints.

Map competitor ecosystems with multi-step chains

`list_competitor_domains` finds companies operating in the exact same market space as your target prospect. Your LangChain agent runs this check, grabs the Datanyze list, and feeds it directly into the next chain link to build a complete competitive map without manual data entry. You can combine this competitor tool with other APIs in a single MultiServerMCPClient instance. The LangChain agent dynamically decides when to pull Datanyze competitor lists and when to query external systems, keeping your market analysis completely autonomous.

Extract verified decision-makers for cold outreach

`get_domain_contacts` resolves specific profiles, job titles, and professional social links for executives at any target domain. Your LangChain agent handles the research, matching the Datanyze contacts against your criteria in a structured output chain. This LangChain setup eliminates the need to manually scrape LinkedIn or export CSVs. The agent calls the Datanyze tool, extracts the verified emails or social links, and passes them to your email drafting step in one clean run.

Setup guide

Set up Datanyze 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 Datanyze 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({
    "datanyze-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 Datanyze 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 Datanyze. 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 Datanyze MCP in LangChain

Install langchain-mcp-adapters and initialize the MultiServerMCPClient. Call client.get_tools() to fetch search_b2b_companies and other endpoints, then pass them directly to your agent constructor.

Yes, you can use get_api_credit_balance inside your chain to monitor your remaining credits. LangSmith tracks every tool call, giving you full visibility into how many credits your agents consume during runs.

The framework catches exceptions during tool execution. If get_domain_intelligence returns an error or empty payload, your agent can read the traceback and decide to try a different domain or log the issue.

Yes, the MultiServerMCPClient aggregates tools from different sources. Your agent can query search_b2b_companies on this server and immediately combine those results with databases connected via other servers.

Your queries for domain technographics and B2B contact records run inside a secure V8 Isolate Sandbox. Vinkius keeps your API tokens encrypted and never logs the specific domains or emails your LangChain agent searches.

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