How to Use the YesNo MCP in LangChain

Q: Can I use YesNo in my LangChain agent to simulate uncertainty?

Yes. You can call getdecision at any point in your chain to introduce a random 'maybe' result, simulating real-world ambiguity for testing purposes.

Q: How do I force YesNo to return 'Yes' using LangChain?

When calling getdecision, you pass the desired answer explicitly. The tool then executes and returns only the forced decision, ignoring randomness.

Let LangChain decide: Incorporate random yes/no outcomes into your reasoning chains.

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Connect YesNo MCP to LangChain

Create your Vinkius account to connect YesNo 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 YesNo with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Integrate Decisions in Your ReAct Agents

The `get_decision` tool provides instant, random answers—yes, no, or maybe. This is perfect for breaking ties deep inside a multi-step reasoning chain. You can also force the outcome by specifying an answer when calling this MCP Server. The output immediately becomes usable input for the next step in your LangChain agent's sequence.

Controlling Agent Output Flow

Your agent needs a clear path forward after gathering data. Use `get_decision` to introduce calculated randomness, forcing the chain down one of three paths. The tool handles generating an accompanying GIF with the result, which gives your agent's output context for human observability.

Handling Uncertain Inputs

Sometimes data gathering doesn't yield a clean answer. `get_decision` resolves that ambiguity by providing a random 'maybe' option. This capability lets you build more resilient LangChain agents that don't halt when faced with inconclusive intermediate results.

Setup guide

Set up YesNo 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 YesNo 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({
    "yesno-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 YesNo 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 YesNo. 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 YesNo MCP in LangChain

Yes. You can call `get_decision` at any point in your chain to introduce a random 'maybe' result, simulating real-world ambiguity for testing purposes.

Absolutely. The tool outputs the decision (yes/no/maybe) and its associated GIF link as a plain string, which your agent consumes instantly for subsequent steps.

When calling `get_decision`, you pass the desired answer explicitly. The tool then executes and returns only the forced decision, ignoring randomness.

Yes, your client supports aggregating multiple servers. You can combine `yesno-mcp` with other tools to build complex reasoning pipelines.

This server primarily touches non-sensitive decision metadata and GIF URLs. The core data type is a randomized yes/no/maybe decision string.

Use it with your favorite AI tools

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