How to Use the Mod.io MCP in LangChain

Q: How do I filter games on Mod.io using LangChain agents?

Use the getgames tool within your tool definition. Your agent processes the raw JSON output to apply custom search logic based on your specific requirements.

Build mod-aware agents in LangChain. Chain your mod.io operations into automated discovery and subscription pipelines.

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Connect Mod.io MCP to LangChain

Create your Vinkius account to connect Mod.io 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 Mod.io with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automated Mod Discovery

Pipe the output of `get_games` directly into `get_mods` to build dynamic game-specific discovery chains. Your agent parses the results to find trending content without manual intervention. This workflow moves data through your LangChain graph instantly. You define the logic, and the agent iterates through the API response until it hits the specific mod criteria you set.

Subscription Logic Chains

Link `subscribe_mod` to your existing agent memory to manage user state. The system confirms subscriptions by checking `get_my_subscriptions` in the next step of the chain. You avoid manual polling by letting the agent trigger these operations only when the logic path requires it. It keeps your event loop tight and predictable.

Cross-Game Data Pipelines

Aggregate data from `get_game_stats` and `get_collections` to compare developer traction across titles. You feed these metrics into your RAG store for long-term analysis. It treats every API response as a single link in a larger reasoning chain. You see exactly how the agent arrives at its decision via your existing tracing tools.

Setup guide

Set up Mod.io 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 Mod.io 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({
    "modio-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 Mod.io 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 Mod.io. 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 Mod.io MCP in LangChain

Use the `get_games` tool within your tool definition. Your agent processes the raw JSON output to apply custom search logic based on your specific requirements.

Yes. Pass your OAuth 2 token through the environment variables or the MCP server configuration. The server handles the header injection for all authenticated calls.

The server remains stateless, but you can persist subscription data using a standard LangChain memory buffer. You store the output of `get_my_subscriptions` to keep track of user state.

Use standard LangSmith tracing. Every tool execution is recorded with full input and output payloads for every request.

We only touch the specific mod metadata and subscription records you explicitly request. Your OAuth token is used strictly for the duration of the API call.

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