How do I get a Medium Integration Token?

Go to Medium Settings > Security and apps > Integration tokens to generate your token.

Can I publish in Markdown?

Yes! The `create_post` and `create_draft` tools support Markdown format natively.

Absolutely. Your token is encrypted at rest and injected securely at runtime.

Medium MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Medium
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Medium as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Medium. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Medium?"
    )
    print(response)

asyncio.run(main())

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About Medium MCP Server

Connect your Medium account to any AI agent and automate your publishing workflow through natural conversation.

LlamaIndex agents combine Medium tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Post Creation — Quickly publish public articles or save drafts directly to your Medium account
Publication Management — List publications you belong to and publish content directly under their brand
Profile Inspection — Retrieve your unique User ID and profile details for seamless integration
Contributor Lists — View authorized contributors for publications you manage

The Medium MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Medium to LlamaIndex via MCP

Follow these steps to integrate the Medium MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Medium

Why Use LlamaIndex with the Medium MCP Server

LlamaIndex provides unique advantages when paired with Medium through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Medium tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Medium tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Medium, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Medium tools were called, what data was returned, and how it influenced the final answer

Medium + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Medium MCP Server delivers measurable value.

Hybrid search: combine Medium real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Medium to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Medium for fresh data

Analytical workflows: chain Medium queries with LlamaIndex's data connectors to build multi-source analytical reports

Medium MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Medium to LlamaIndex via MCP:

create_draft

Create a new draft

create_post

Create a new post for a user

create_public_post

Create a public post

create_publication_post

Create a post under a publication

get_authenticated_user

Get details for the authenticated user

get_my_profile

Get your own profile

get_my_user_id

Get your User ID

list_contributors

List contributors for a publication

list_my_publications

List your own publications

list_publications

List publications for a user

Example Prompts for Medium in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Medium immediately.

"Show my Medium profile and user ID."

"Create a draft titled 'My AI Journey' with content 'This is my first post...'"

"List my publications."

Troubleshooting Medium MCP Server with LlamaIndex

Common issues when connecting Medium to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Medium + LlamaIndex FAQ

Common questions about integrating Medium MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Medium tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.