How to Use the Modash MCP in LangChain

Build multi-step social prospecting chains that find and audit creators automatically using LangChain and this MCP Server.

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Connect Modash MCP to LangChain

Create your Vinkius account to connect Modash 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 Modash with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-platform creator discovery via LangChain

The `search_instagram`, `search_tiktok`, and `search_youtube` tools let your LangChain agent scan social platforms for creators matching your criteria. By taking your campaign requirements, the agent queries all three platforms and filters the raw list. You get a clean, deduplicated dataset directly in your LangChain pipeline without writing custom API wrappers for each social platform.

Automated multi-step vetting pipelines

The `get_instagram_report` tool pulls deep profile analytics directly into your LangChain routing chains. Once `search_instagram` finds a prospect, the output feeds straight into the report tool to check engagement rates and fake followers. This setup lets your LangChain agent make decisions on the fly, dropping low-engagement candidates and logging the entire path in LangSmith.

Contextual targeting with platform metadata

The `list_interests` and `list_locations` tools provide structured platform metadata directly to your LangChain agent. This prevents empty search results and wasted API calls during automated execution. The LangChain agent matches your natural language prompt to Modash's structured categories, ensuring your search queries are highly accurate from the first run.

Setup guide

Set up Modash 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 Modash 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({
    "modash-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 Modash 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 Modash. 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 Modash MCP in LangChain

You install the langchain-mcp-adapters package and connect to the server URL. Use the MultiServerMCPClient to retrieve the tools, then pass them directly into your LangChain agent's tool list.

Yes. You can configure your LangChain runnable map to execute `search_instagram` and `search_youtube` concurrently, speeding up your creator discovery process.

You can implement exponential backoff directly in your LangChain runnable configuration. This ensures that rapid calls to `get_tiktok_report` do not trigger API errors during deep vetting runs.

Yes, you can mix them easily. Your LangChain chain can search for creators, run `get_raw_profile` for fresh metrics, and write the qualifiers directly to your database in a single sequence.

The server only processes public social media profile metrics and audience demographic data. All requests run through Vinkius's secure sandboxed environment, meaning your API keys and search parameters are never exposed or stored.

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