How to Use the AeroLeads MCP in LangChain

Q: How do I connect the AeroLeads MCP Server to LangChain?

Install langchain-mcp-adapters and langgraph. Initialize a MultiServerMCPClient pointing to your Vinkius endpoint, then pass client.gettools() to your ReAct agent.

Q: Can LangChain agents check my AeroLeads credit balance?

Yes. Your agent will call getcreditbalance before running a massive domain search. This prevents the chain from failing halfway through a large prospecting run.

Q: How does AeroLeads work with LangChain memory?

By default, the MCP client is stateless. You need to use client.session() if you want the agent to remember which leads it already pulled using listcapturedleads across multiple turns.

Build multi-step prospecting pipelines in LangChain that find emails, enrich LinkedIn data, and manage your pipeline automatically.

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Connect AeroLeads MCP to LangChain

Create your Vinkius account to connect AeroLeads 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 AeroLeads with LangChain

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Chain prospect discovery with LangChain

Connect your ReAct agents directly to this MCP Server for B2B contact data. When you call `search_domain_leads`, the agent pulls a raw list of potential targets from a target company. It then feeds those names into `find_email_by_name` to secure verified contact info. The output of one step becomes the exact input for the next. Your agent handles the reasoning process entirely, deciding which prospects match your ideal customer profile before moving them down the chain.

Automate list building via MCP Server

Stop manually copying and pasting contact details into spreadsheets. You'll configure a LangGraph pipeline that evaluates leads and uses `add_new_lead` to push qualified targets straight into your lists. If a prospect bounces or leaves a company, the agent triggers `delete_lead` to keep your database clean. Managing your sales pipeline happens through code, removing human error from list maintenance.

Track credit spend in LangSmith

Prospecting APIs cost money, so you need visibility into how often your agents hit the endpoints. LangSmith tracing logs every tool invocation, showing you exactly when the agent calls `get_credit_balance`. You see the exact token usage and latency for every data request. If an agent loops on `get_linkedin_details`, you catch it immediately and adjust the system prompt before burning through your account limits.

Setup guide

Set up AeroLeads 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 AeroLeads 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({
    "aeroleads-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 AeroLeads 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 AeroLeads. 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 AeroLeads MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph`. Initialize a `MultiServerMCPClient` pointing to your Vinkius endpoint, then pass `client.get_tools()` to your ReAct agent.

Yes. Your agent will call `get_credit_balance` before running a massive domain search. This prevents the chain from failing halfway through a large prospecting run.

By default, the MCP client is stateless. You need to use `client.session()` if you want the agent to remember which leads it already pulled using `list_captured_leads` across multiple turns.

The agent receives the full JSON response. You should prompt your model to paginate or filter the results before passing them to the next tool to avoid exceeding token windows.

Vinkius runs the integration inside a V8 Isolate Sandbox. When your agent pulls business emails and phone numbers, that sensitive contact data flows through an ephemeral environment that destroys itself after the request finishes.

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