How do I obtain a Feedly Developer Token?

Log in to your Feedly account and navigate to the **Settings > Developer** page to generate a personal access token for prototyping and individual use.

Can I mark articles as read using this agent?

Yes! Use the `mark_as_read` tool with the entry IDs of the articles you wish to clear from your unread stream.

Is it possible to search for new feeds through the agent?

Absolutely. The `search_feeds` tool allows you to search the Feedly global index for specific topics or site URLs to add to your collections.

Feedly MCP Server for LangChain 12 tools — connect in under 2 minutes

Name: Feedly
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

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.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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": {
            "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())

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* 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 consumption and RSS aggregation through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Feedly through native MCP adapters. Connect 12 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

Collection Orchestration — List all your curated collections and feeds to organize your information flow natively
Stream Intelligence — Retrieve the latest articles from specific feeds or entire categories with full metadata flawlessly
Read State Management — Mark articles as read or save them for later directly from the cloud without manual UI interaction
Content Discovery — Search for new RSS feeds and trending topics across the entire Feedly index flawlessly
Board & Tag Organization — List and query articles from your personal boards and tagged content natively
User Insights — Access your Feedly profile and subscription metadata through the agent synchronously

The Feedly MCP Server exposes 12 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.

How to Connect Feedly to LangChain via MCP

Follow these steps to integrate the Feedly MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 12 tools from Feedly via MCP

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

Feedly MCP Tools for LangChain (12)

These 12 tools become available when you connect Feedly to LangChain via MCP:

get_board_contents

Retrieve articles from a specific board

get_entry

Get details for a specific article entry

get_profile

Get current Feedly user profile

get_stream_contents

Retrieve articles for a specific stream (feed, category, or global)

get_subscriptions

List all individual feed subscriptions

get_tag_contents

Retrieve articles associated with a specific tag

list_boards

List all your Feedly boards (saved for later)

list_collections

List all your Feedly collections (categories) and feeds

list_tags

List all your Feedly tags

mark_as_read

Mark specific articles as read

search_feeds

Search for new RSS feeds in the Feedly index

search_topics

Search for trending topics or specific interests

Example Prompts for Feedly in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Feedly immediately.

"List my Feedly collections."

"Show me the latest 5 articles from the 'Tech News' category."

"Search for feeds about 'Edge Computing'."

Troubleshooting Feedly MCP Server with LangChain

Common issues when connecting Feedly to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Feedly + LangChain FAQ

Common questions about integrating Feedly MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.