Feedly MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
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.
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Vinkius supports streamable HTTP and SSE.
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 12 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 consumption and RSS aggregation through natural conversation.
LlamaIndex agents combine Feedly tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- 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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Feedly MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from Feedly
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
Feedly MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Feedly to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 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.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Feedly with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
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Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Feedly to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.