Feedly MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Get Article Details, Get Feed Metadata, Get Stream Contents, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Feedly 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 Feedly 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 Feedly. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Feedly?"
)
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 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.
LlamaIndex agents combine Feedly 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 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 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 Feedly tools available for LlamaIndex
When LlamaIndex 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 LlamaIndex via MCP
Follow these steps to wire Feedly 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 Feedly MCP Server
LlamaIndex provides unique advantages when paired with Feedly through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Feedly tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Feedly tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Feedly, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Feedly tools were called, what data was returned, and how it influenced the final answer
Feedly + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Feedly MCP Server delivers measurable value.
Hybrid search: combine Feedly real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Feedly 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 Feedly for fresh data
Analytical workflows: chain Feedly queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Feedly in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Feedly to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFeedly + LlamaIndex FAQ
Common questions about integrating Feedly MCP Server with LlamaIndex.