How to Use the Jellyfin (Plex Alternative) MCP in LlamaIndex

Index your media library with LlamaIndex using the Jellyfin (Plex Alternative) MCP Server.

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Connect Jellyfin (Plex Alternative) MCP to LlamaIndex

Create your Vinkius account to connect Jellyfin (Plex Alternative) 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 Jellyfin (Plex Alternative) with LlamaIndex

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Claude

Perplexity

Semantic indexing of media collections

Convert your library output from `list_items` into a vector index for natural language querying. LlamaIndex stores this data to provide grounded answers about your collection. Instead of just listing files, your agent searches your library semantically. It finds content based on descriptions and metadata rather than exact string matches.

Ground AI responses in media metadata

Use `get_item` to fetch granular details that LlamaIndex then indexes for your RAG pipeline. The agent provides accurate information about movies and shows by pulling directly from your live server. This creates a knowledge base rooted in your actual library. Your agent stops hallucinating about media details and speaks only to what exists on your disk.

Unified search for Jellyfin (Plex Alternative) data

Combine API results with local documents by integrating your MCP Server tools into a single searchable index. This provides a unified view of your media and related notes. The agent queries this combined index to answer questions that span across your library and external data sources. It treats your media metadata as a first-class knowledge asset.

Setup guide

Set up Jellyfin (Plex Alternative) 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 Jellyfin (Plex Alternative) 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 Jellyfin (Plex Alternative) tools.",
)
response = await agent.run("List recent Jellyfin (Plex Alternative) data")

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Common questions about Jellyfin (Plex Alternative) MCP in LlamaIndex

The McpToolSpec retrieves your library items and converts them into a format suitable for vector storage. This allows your agent to perform semantic searches across your entire media catalog.

Absolutely. By passing these tools to a FunctionAgent, your RAG application gains the ability to query live server data as part of its retrieval process.

It is. You can filter tools to include only the ones you need, keeping your index focused and reducing unnecessary API calls to your server.

It stores metadata and descriptions in a vector index for retrieval purposes. The actual media files remain on your Jellyfin server and are not copied to the vector store.

Credentials are used to establish a secure, authenticated connection to your server. This sensitive data is kept in your local environment variables and is not exposed to the indexing process.

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