How to Use the Feedly MCP in CrewAI

Q: Can CrewAI agents use Feedly for intelligence?

Yes, they can. By using getarticledetails, your agents can ingest full reports and feed that knowledge into the rest of the crew for synthesis.

Q: Can I use Feedly categories in CrewAI?

You can. Use listcategories to define the scope for your agents. This lets you isolate specific research topics to different crews for better precision.

Deploy specialized research crews for Feedly data management using this MCP Server.

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Connect Feedly MCP to CrewAI

Create your Vinkius account to connect Feedly to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Feedly with CrewAI

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Multi-agent research crews for Feedly

Assign a research agent to pull data via `get_stream_contents` while a separate analyst agent reviews the output. This division of labor keeps your research process organized and fast. It allows for depth. Your crew can handle hundreds of feeds simultaneously, summarizing findings without you needing to lift a finger.

Curated feed monitoring in CrewAI

Use `get_feed_metadata` to let your monitor agent decide which feeds are worth tracking. If a source loses relevance, the agent can call `unsubscribe_from_feed` to keep the crew focused. This is autonomous maintenance. Your research environment stays clean and relevant without human intervention.

Collaborative article analysis with CrewAI

Your agents can share memory to track what has already been read. By invoking `mark_articles_as_read` after analysis, the crew ensures no article is processed twice. It prevents redundancy. Your agents work in sync, providing a clear picture of the industry landscape through shared, updated state.

Setup guide

Set up Feedly MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Feedly tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Feedly Analyst",
    goal="Access and analyze Feedly data via MCP.",
    backstory="Expert analyst with direct Feedly access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Feedly transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Feedly Analyst",
        goal="Access and analyze Feedly data via MCP.",
        backstory="Expert analyst with direct Feedly access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Feedly transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

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.

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Common questions about Feedly MCP in CrewAI

Add the server URL to your agent's MCP configuration. CrewAI will then expose the tools to your agents, allowing them to access your feeds directly.

Yes, they can. By using `get_article_details`, your agents can ingest full reports and feed that knowledge into the rest of the crew for synthesis.

It does. You can give a 'Researcher' agent exclusive access to feed tools while a 'Manager' agent handles the subscription list, keeping duties distinct.

All your article content and profile settings are handled via secure, ephemeral tokens. No data is cached outside of your active agent session.

You can. Use `list_categories` to define the scope for your agents. This lets you isolate specific research topics to different crews for better precision.

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