How to Use the Landing MCP in CrewAI

Q: How do I configure Landing tools for a CrewAI agent?

Install the MCP extras via pip install crewaitools. Then pass your server URL directly into the mcps array on your specific agent definition.

Q: How do I restrict a CrewAI agent to only read Landing data?

Use MCPServerHTTP and apply a toolfilter. You can expose listlandingpages while hiding the webhook deletion tools from your junior agents.

Deploy a CrewAI agent team to monitor, audit, and manage your Landing pages and leads autonomously via MCP.

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Connect Landing MCP to CrewAI

Create your Vinkius account to connect Landing 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 Landing with CrewAI

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Analyze Prospect Data

The `list_landing_leads` tool feeds raw prospect data directly to your CrewAI analysis agents. One agent pulls the list via the MCP Server, a second agent scores the contacts, and a third agent triggers an alert. The entire sequence runs without human input. You stop manually exporting CSV files. The crew wakes up, grabs the new submissions, and does the heavy lifting. You just read the final report.

Manage Webhooks via CrewAI and MCP

Manage your webhook infrastructure with a dedicated technical agent. You can assign the `create_landing_webhook` and `list_landing_webhooks` tools to an integration specialist role. It checks for broken links and replaces them automatically. Instead of giving every agent access to everything, you isolate capabilities. The integration agent handles the pipes. The analysis agent handles the data.

Cross-Reference Campaigns

Give your marketing oversight agent full visibility into your active campaigns. By combining `list_landing_pages` and `list_landing_projects`, the agent cross-references live pages against your project database. It flags anything that looks out of place. It checks `get_my_landing_profile` to confirm identity context before running the audit. The crew knows exactly which workspace it is evaluating before it starts pulling metrics.

Setup guide

Set up Landing 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 Landing tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Install the MCP extras via `pip install crewai[tools]`. Then pass your server URL directly into the `mcps` array on your specific agent definition.

You can set up a sequential crew that runs on a cron job. The first agent executes `list_landing_leads` and passes the context down the chain for processing.

Use `MCPServerHTTP` and apply a `tool_filter`. You can expose `list_landing_pages` while hiding the webhook deletion tools from your junior agents.

The framework catches the error and can either retry the task or pass the failure state to a moderator agent for review.

Only the specific agent assigned to the tool sees the raw profile data. The Vinkius infrastructure processes the HTTP transport in memory, leaving no persistent footprint of your contact lists.

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