Feedly MCP Server for LangChainGive LangChain instant access to 10 tools to Get Article Details, Get Feed Metadata, Get Stream Contents, and more
LangChain is the leading Python framework for composable LLM applications. Connect Feedly 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 Feedly 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({
"feedly-alternative": {
"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 Feedly, 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 Feedly MCP Server
Connect your Feedly account to any AI agent and take full control of your news aggregation and content curation workflows through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Feedly 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 Orchestration — List and manage your subscribed news sources programmatically, including adding or removing RSS/Atom feeds
- Stream Intelligence — Retrieve the latest entries (articles) from specific feeds or categories and monitor unread counts in real-time
- Content Extraction — Programmatically fetch complete article text and metadata to perform deep analysis and summaries via your agent
- Organization Control — Manage your Feedly categories and personal tags to maintain a structured and high-fidelity reading environment
- Reading Workflow — Mark articles as read and manage your reading list programmatically to streamline your news consumption
The Feedly 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 Feedly tools available for LangChain
When LangChain connects to Feedly through Vinkius, your AI agent gets direct access to every tool listed below — spanning rss-aggregator, content-curation, industry-trends, 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.
Get full content of an article
Get metadata for a specific feed
Retrieve articles from a stream
Get your Feedly profile
List your Feedly categories
List all subscribed feeds
List your personal tags
Mark one or more articles as read
Follow a new news source
Stop following a news source
Connect Feedly to LangChain via MCP
Follow these steps to wire Feedly 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 Feedly MCP Server
LangChain provides unique advantages when paired with Feedly through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Feedly 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 Feedly queries for multi-turn workflows
Feedly + LangChain Use Cases
Practical scenarios where LangChain combined with the Feedly MCP Server delivers measurable value.
RAG with live data: combine Feedly tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Feedly, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Feedly tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Feedly tool call, measure latency, and optimize your agent's performance
Example Prompts for Feedly in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Feedly immediately.
"List all my categories in Feedly."
"Show me the last 3 unread articles in the 'AI & ML' category."
"Subscribe to this feed: 'https://example.com/rss' and add it to 'Tech'."
Troubleshooting Feedly MCP Server with LangChain
Common issues when connecting Feedly to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFeedly + LangChain FAQ
Common questions about integrating Feedly 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.