How to Use the Inoreader MCP in LlamaIndex

Q: Can LlamaIndex search my Inoreader history?

Yes, it can. Once you ingest articles via liststreamcontents, your LlamaIndex vector store can perform semantic searches across that content.

Q: How do I update Inoreader from LlamaIndex?

You can use tools like edittag or markallasread directly from your agent to modify your feed state.

Q: Can I query unread counts in LlamaIndex?

Yes, you can call getunreadcounts to see how many items are waiting. You can even build an agent that alerts you when counts exceed a threshold.

Q: What data does the Inoreader MCP server store?

It stores nothing. Every request for liststreamcontents or listsubscriptions is a live, ephemeral fetch from the Inoreader API.

Index your live news feeds into LlamaIndex for semantic search and grounded answers based on your actual Inoreader subscriptions.

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Connect Inoreader MCP to LlamaIndex

Create your Vinkius account to connect Inoreader 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 Inoreader with LlamaIndex

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Vectorize news streams in LlamaIndex

Fetch articles using `list_stream_contents` and feed them into your vector store. This lets you query your news history using natural language instead of keywords. Your LlamaIndex pipeline turns raw text from these feeds into searchable knowledge. It bridges the gap between static documents and live RSS data.

Sync your Inoreader tags with LlamaIndex

Use `list_tags` to understand how your content is categorized. You can then create specific indexes for different tags to keep your search results relevant. Updating your organization is simple with `edit_tag`. Your index reflects these changes once the agent performs a re-sync.

Automate subscription discovery in LlamaIndex

Add new sources to your index by triggering `quick_add_subscription`. Your agent can find and subscribe to relevant feeds based on your current knowledge gaps. Verify your new subscriptions with `list_subscriptions` to ensure they are active. This maintains a fresh knowledge base for your RAG applications.

Setup guide

Set up Inoreader 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 Inoreader 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 Inoreader tools.",
)
response = await agent.run("List recent Inoreader 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 Inoreader. 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 Inoreader MCP in LlamaIndex

Yes, it can. Once you ingest articles via `list_stream_contents`, your LlamaIndex vector store can perform semantic searches across that content.

You can use tools like `edit_tag` or `mark_all_as_read` directly from your agent to modify your feed state.

The MCP server only touches your subscription and article data. Your private credentials are never exposed to the vector index itself.

Yes, you can call `get_unread_counts` to see how many items are waiting. You can even build an agent that alerts you when counts exceed a threshold.

It stores nothing. Every request for `list_stream_contents` or `list_subscriptions` is a live, ephemeral fetch from the Inoreader API.

Use it with your favorite AI tools

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