How to Use the Medium MCP in LangChain

Q: Can LangChain agents automatically list my Medium publications?

Yes, the agent calls listmypublications to discover your available outlets. It then selects the right one based on your specific chain instructions.

Q: Does this Medium server support multi-step LangChain pipelines?

It absolutely does. You can chain createdraft followed by a review step before finally executing createpost.

Build automated content chains in LangChain by connecting your agent directly to Medium for draft creation and publication management.

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Connect Medium MCP to LangChain

Create your Vinkius account to connect Medium 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 Medium with LangChain

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Chain publication tasks in LangChain

Your agent uses `create_draft` to generate content before passing that text through a secondary processing chain for final polish. You define the logic flow where the output of one step feeds directly into the next. Everything remains observable via LangSmith. You track exactly when your LangChain agent triggers `create_publication_post` and verify the latency of every API call in the sequence.

Manage Medium workflows with MCP tools

Use `list_my_publications` to retrieve your current writing spaces and identify the correct ID for your next post. Your LangChain agent handles the enumeration of target destinations without manual intervention. This creates a pipeline where your agent fetches data, selects the publication, and pushes the final article. It keeps your development environment clean and your publishing logic strictly defined.

Secure user identity for LangChain

The `get_authenticated_user` tool allows your agent to confirm its identity before attempting any write operations. It ensures your LangChain application is operating with the correct credentials. You avoid common API errors by checking your profile status first. It gives your agent the context it needs to perform actions on your behalf with full awareness of your account permissions.

Setup guide

Set up Medium 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 Medium 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({
    "medium-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 Medium 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 Medium. 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 Medium MCP in LangChain

You provide the integration token through your environment configuration. Your LangChain instance then uses this to authorize every tool call.

Yes, the agent calls `list_my_publications` to discover your available outlets. It then selects the right one based on your specific chain instructions.

It absolutely does. You can chain `create_draft` followed by a review step before finally executing `create_post`.

All tool inputs and outputs are captured in your LangSmith traces. You get a clear view of how your agent interacts with the API.

The server only touches your post drafts and publication metadata. It never stores your credentials, as your LangChain client manages the session token locally.

Use it with your favorite AI tools

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