Feedly MCP Server for Pydantic AIGive Pydantic AI instant access to 10 tools to Get Article Details, Get Feed Metadata, Get Stream Contents, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Feedly through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Feedly app connector for Pydantic AI 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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Feedly "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Feedly?"
)
print(result.data)
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.
Pydantic AI validates every Feedly tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI
When Pydantic AI 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 Pydantic AI via MCP
Follow these steps to wire Feedly into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Feedly MCP Server
Pydantic AI provides unique advantages when paired with Feedly through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Feedly integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Feedly connection logic from agent behavior for testable, maintainable code
Feedly + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Feedly MCP Server delivers measurable value.
Type-safe data pipelines: query Feedly with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Feedly tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Feedly and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Feedly responses and write comprehensive agent tests
Example Prompts for Feedly in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Feedly to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiFeedly + Pydantic AI FAQ
Common questions about integrating Feedly MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.