How to Use the Medium MCP in LlamaIndex

Q: How do I filter tools for my LlamaIndex agent?

You use the allowedtools parameter when initializing your agent. This restricts the agent to only the specific Medium tools you want to expose.

Turn Medium content into searchable knowledge in LlamaIndex by indexing your posts and drafts directly into your vector store.

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Connect Medium MCP to LlamaIndex

Create your Vinkius account to connect Medium to LlamaIndex 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 LlamaIndex

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Index Medium data for LlamaIndex

Use `list_publications` to pull your existing content into a format your agent can ingest. LlamaIndex then vectorizes this data for semantic search across your entire writing history. You stop hallucinating about past content because your agent queries the live API. It grounds every answer in the actual text of your published work.

Automate drafts with LlamaIndex tools

Your agent triggers `create_draft` based on questions derived from your indexed knowledge base. It creates new content that aligns with your established writing style. This creates a closed-loop system where your past work informs your future output. You build RAG applications that read your own Medium feed to generate relevant new drafts.

Verify contributor access in LlamaIndex

The `list_contributors` tool provides visibility into who else has access to your publications. Your LlamaIndex agent checks these permissions before attempting to push updates. This prevents unauthorized write attempts during automated tasks. It keeps your publishing process secure by verifying the team structure before executing any commands.

Setup guide

Set up Medium MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Medium MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Medium tools.",
)
response = await agent.run("List recent Medium data")

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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 LlamaIndex

The server retrieves post text via the API tools. LlamaIndex then processes this text into a searchable vector index for your agent.

You index your history by calling the list tools. Once indexed, your LlamaIndex application performs semantic lookups against that data.

It is built for that exact purpose. You pull live data from the API and feed it directly into your LlamaIndex query engine.

You use the allowed_tools parameter when initializing your agent. This restricts the agent to only the specific Medium tools you want to expose.

The server only processes your post text and publication metadata. Your LlamaIndex instance handles the data locally and does not share it with external parties.

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