How to Use the Feedly MCP in LlamaIndex

Q: How do I index Feedly article content into LlamaIndex?

Use getstreamcontents to pull the latest articles, then pass the text nodes into your LlamaIndex vector store index. This creates a searchable, local database of your daily news.

Q: Can LlamaIndex search my Feedly tags?

Yes. The agent calls listtags to retrieve your custom labels, allowing LlamaIndex to filter or categorize indexed articles based on your existing organizational structure.

Index live Feedly streams directly into your LlamaIndex vector stores for hallucination-free research.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Feedly MCP to LlamaIndex

Create your Vinkius account to connect Feedly 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.

GDPR Free for Subscribers

Setup Feedly with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Feedly MCP Server data for RAG pipelines

This server exposes `get_stream_contents` and `get_article_details` to let LlamaIndex query your live RSS feeds and index the results on the fly. Stop building RAG applications on stale PDFs. Your agent pulls actual article text directly into your vector index. This setup ensures your LLM answers are grounded in real-time industry news. When you ask a question, LlamaIndex retrieves context from your active subscriptions instead of relying on outdated training data.

Query your feed metadata semantically

By exposing `list_subscriptions` and `get_feed_metadata`, this server lets LlamaIndex query your feed metadata semantically. Finding the right feed inside a massive account is slow. Your agent can search your sources using natural language rather than strict IDs. The `McpToolSpec` adapter converts these tools into a format LlamaIndex understands. Your agent can search across your custom categories from `list_categories` and find the exact feed that matches your current research query.

Automate feed curation based on index gaps

This server exposes `subscribe_to_feed` to let your LlamaIndex agent manage your information flow based on index gaps. Let your agent handle your information flow. If your vector search yields low-confidence results on a specific topic, the agent can add new industry sources. Once the new feed is added, the agent pulls the latest content and updates the index. If a source consistently provides low-quality or off-topic articles, the agent can clean up your feed list using `unsubscribe_from_feed`.

Setup guide

Set up Feedly 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 Feedly 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 Feedly tools.",
)
response = await agent.run("List recent Feedly data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Feedly. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Feedly now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Feedly MCP in LlamaIndex

Use `get_stream_contents` to pull the latest articles, then pass the text nodes into your LlamaIndex vector store index. This creates a searchable, local database of your daily news.

Yes. The agent calls `list_tags` to retrieve your custom labels, allowing LlamaIndex to filter or categorize indexed articles based on your existing organizational structure.

You initialize the client with your Vinkius endpoint, then pass it to `McpToolSpec`. This exposes the 10 Feedly tools directly to your LlamaIndex `FunctionAgent` for execution.

Yes. After your agent indexes and processes an article, it can call `mark_articles_as_read` to keep your Feedly inbox clean and avoid duplicate processing.

Your profile details and RSS data are processed in ephemeral, zero-trust environments. Vinkius never caches your subscription lists, and all API communication is encrypted in transit to protect your reading habits.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript