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 CrewAI 12 tools — connect in under 2 minutes

Name: Feedly
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Feedly through Vinkius, pass the Edge URL in the `mcps` parameter and every Feedly tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Feedly Specialist",
    goal="Help users interact with Feedly effectively",
    backstory=(
        "You are an expert at leveraging Feedly tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Feedly "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Feedly becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Feedly tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 12 tools from Feedly

Why Use CrewAI with the Feedly MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Feedly through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Feedly + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Feedly MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Feedly for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Feedly, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Feedly tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Feedly against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Feedly MCP Tools for CrewAI (12)

These 12 tools become available when you connect Feedly to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Feedly + CrewAI FAQ

Common questions about integrating Feedly MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.