How to Use the Influencers Club MCP in LangChain

Q: Can I use Influencers Club with LangChain using an MCP Server?

Yes, you can register enrichbyemail as a tool inside a LangChain state machine. The graph routes the email to the tool, extracts the social handle, and passes it to the next node for automated draft generation.

Q: How do I monitor token usage for Influencers Club MCP Server tools in LangChain?

You use LangSmith to trace every tool call, including getcreatorposts. This shows you the exact payload size and token count consumed by your agent during analysis.

Run multi-step creator discovery chains in LangChain using verified social metrics from the Influencers Club MCP Server.

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Connect Influencers Club MCP to LangChain

Create your Vinkius account to connect Influencers Club 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 Influencers Club with LangChain

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Claude

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Chain Creator Queries in LangChain

The `search_creators` tool feeds raw social filters directly into your LangChain decision chains so your agent can source matching profiles without manual search. Your agent analyzes the initial search output, decides if the follower count fits your budget, and automatically pipes those handles into `enrich_by_handle` to pull complete social profiles. You configure these steps as sequential links inside a LangChain RunnableSequence. This setup lets the agent evaluate real-time creator data, run conditional logic based on engagement rates, and output a curated CSV of targets ready for outreach.

Trace Audience Overlap with LangSmith

The `get_audience_overlap` tool runs deep audience comparisons across multiple social handles directly within your LangChain pipeline. You monitor every single API call and token cost in LangSmith, which gives you clear visibility into how your agent processes complex audience overlaps. When your agent identifies two creators with high overlap, it automatically triggers `get_similar_creators` to find alternative options. This entire execution path is logged step-by-step, showing you exactly how the agent navigated the decision tree to find the best lookalike targets.

Automate Credit Allocation in Multi-Step Chains

The `get_user_info` tool checks your remaining credits before your LangChain agent kicks off a massive enrichment run. The agent queries your account status first, stopping the chain or alerting your Slack channel if your balance runs low. By coupling this with `get_usage`, your LangChain agent tracks daily consumption metrics across long-running loops. You prevent wasted API calls and ensure your automated outreach campaigns never hit a hard wall mid-execution.

Setup guide

Set up Influencers Club 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 Influencers Club 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({
    "influencers-club-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 Influencers Club 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 Influencers Club. 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 Influencers Club MCP in LangChain

The server returns standard rate limit headers that your LangChain agent intercepts. You can configure a custom retry parser in your Runnable sequence to pause execution when limits are approached.

Yes, you can register `enrich_by_email` as a tool inside a LangChain state machine. The graph routes the email to the tool, extracts the social handle, and passes it to the next node for automated draft generation.

You use LangSmith to trace every tool call, including `get_creator_posts`. This shows you the exact payload size and token count consumed by your agent during analysis.

Yes, the MultiServerMCPClient lets you combine this server with database or CRM servers in one LangChain setup. Your agent can pull creator data using `search_creators` and write it directly to your Postgres database in a single chain execution.

Your API keys and social handle queries are processed entirely inside the Vinkius V8 Isolate Sandbox. No raw profile data or account credentials are saved or exposed to external networks during execution.

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