Inoreader MCP Server for LangChainGive LangChain instant access to 10 tools to Delete Tag, Edit Tag, Get Unread Counts, and more
LangChain is the leading Python framework for composable LLM applications. Connect Inoreader through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The Inoreader app connector for LangChain 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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"inoreader": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Inoreader, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Inoreader through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain
When LangChain 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 LangChain via MCP
Follow these steps to wire Inoreader into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Inoreader MCP Server
LangChain provides unique advantages when paired with Inoreader through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Inoreader MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Inoreader queries for multi-turn workflows
Inoreader + LangChain Use Cases
Practical scenarios where LangChain combined with the Inoreader MCP Server delivers measurable value.
RAG with live data: combine Inoreader tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Inoreader, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Inoreader tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Inoreader tool call, measure latency, and optimize your agent's performance
Example Prompts for Inoreader in LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Inoreader to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersInoreader + LangChain FAQ
Common questions about integrating Inoreader MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.