Inoreader MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Delete Tag, Edit Tag, Get Unread Counts, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Inoreader as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Inoreader app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Inoreader. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Inoreader?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Inoreader MCP Server
Connect your Inoreader account to any AI agent and transform how you monitor news, blogs, and social feeds through natural language control.
LlamaIndex agents combine Inoreader 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
- Feed Management — List all your subscriptions and quickly add new RSS/Atom feeds by URL.
- Content Extraction — Fetch article contents from specific feeds, folders, or system streams with advanced filtering.
- Organization — List, create, rename, and delete tags or folders to keep your information architecture clean.
- Engagement — Star important articles, mark items as read, or batch-clear entire streams instantly.
- Unread Monitoring — Get real-time summaries of unread counts across all your categorized content.
The Inoreader 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.
All 10 Inoreader tools available for LlamaIndex
When LlamaIndex connects to Inoreader through Vinkius, your AI agent gets direct access to every tool listed below — spanning rss-reader, content-curation, news-monitoring, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Articles will remain but the organizational label is removed. Delete a tag or folder
Use "user/-/state/com.google/starred" to star/unstar an item. Add or remove tags from articles (e.g., Starred)
Get the number of unread items per feed/folder
Get Inoreader user information
Use "user/-/state/com.google/reading-list" for all items. Get articles for a specific feed, folder, or tag
List all user subscriptions (feeds)
List all user tags and folders
Mark all items in a stream as read
Subscribe to a new feed by URL
Rename an existing tag or folder
Connect Inoreader to LlamaIndex via MCP
Follow these steps to wire Inoreader into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Inoreader MCP Server
LlamaIndex provides unique advantages when paired with Inoreader through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Inoreader tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Inoreader tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Inoreader, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Inoreader tools were called, what data was returned, and how it influenced the final answer
Inoreader + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Inoreader MCP Server delivers measurable value.
Hybrid search: combine Inoreader real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Inoreader 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 Inoreader for fresh data
Analytical workflows: chain Inoreader queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Inoreader in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Inoreader immediately.
"What are the latest news from my Tech folder?"
"Find articles about 'SpaceX' that I haven't read yet."
"Mark all articles in my 'Social Media' tag as read."
Troubleshooting Inoreader MCP Server with LlamaIndex
Common issues when connecting Inoreader to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpInoreader + LlamaIndex FAQ
Common questions about integrating Inoreader MCP Server with LlamaIndex.