How to Use the Amplenote MCP in CrewAI

Q: How do I restrict a specific CrewAI agent from deleting Amplenote notes?

Use MCPServerHTTP from crewai.mcp and apply a toolfilter. This lets you expose read-only tools to your research agents while keeping deletenote restricted.

Let specialized CrewAI agent teams collaborate to organize your Amplenote workspace via MCP.

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Connect Amplenote MCP to CrewAI

Create your Vinkius account to connect Amplenote 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 Amplenote with CrewAI

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Coordinate Multi-Agent Teams with CrewAI

Running `search_notes` lets your research agent find raw meeting transcripts and hand them off to your editor agent. This multi-agent setup ensures that raw text gets cleaned, formatted, and tagged before it ever hits your workspace, keeping your documentation organized without you having to write a single word of markdown.

Build Autonomous Task Crews via CrewAI MCP Server

Calling `list_tasks` allows your monitoring agent to check for overdue items and coordinate with your execution agents. Because CrewAI supports shared memory, the agents remember which tasks they modified earlier in the session. They won't get stuck in loops updating the same due dates repeatedly.

Align Workspace Taxonomy with CrewAI

Querying your current system with `list_tags` lets your taxonomy crew audit and flag redundant labels across your database. The crew then executes `get_note` to read affected documents and applies the corrected tags, ensuring your entire team uses the exact same organizational structure.

Setup guide

Set up Amplenote 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 Amplenote tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Amplenote 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="Amplenote Analyst",
        goal="Access and analyze Amplenote data via MCP.",
        backstory="Expert analyst with direct Amplenote access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Amplenote 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 Amplenote. 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 Amplenote MCP in CrewAI

You can pass your Vinkius HTTP endpoint directly into the agent's `mcps` array parameter. CrewAI automatically discovers and configures tools like `list_notes` for that specific agent.

Yes, your crew shares access to the tools. One agent can read a document using `get_note` while another agent simultaneously creates a follow-up action using `create_task`.

Use `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. This lets you expose read-only tools to your research agents while keeping `delete_note` restricted.

Yes, CrewAI supports Streamable HTTP and SSE transports natively. It communicates with your Vinkius-hosted server using standard web protocols.

Your workspace data, including note text, task deadlines, and tags, is handled entirely within Vinkius's zero-trust MCP environment. CrewAI only receives the specific data payloads requested by active tools, with zero persistent storage.

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