How to Use the GroundX MCP in CrewAI

Q: How do I add GroundX tools to a CrewAI agent?

You pass the GroundX URL to your agent's mcps parameter. This makes tools like searchdocuments available to that specific agent immediately.

Deploy autonomous agent crews using GroundX to automate research, monitoring, and document analysis tasks.

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Connect GroundX MCP to CrewAI

Create your Vinkius account to connect GroundX 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 GroundX with CrewAI

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Autonomous research crews with GroundX

Assign a dedicated research agent to crawl and ingest data using `ingest_website` while your analyst agent performs `search_content`. CrewAI manages the handover, ensuring your researchers gather facts before your writers summarize them. This creates a self-sustaining loop. The crew identifies gaps in knowledge, triggers ingestion, and then searches the updated index to refine its final output.

Specialized agent roles

Give your moderator agent access to `get_ingest_status` to oversee the health of your data pipelines. If the monitor reports that documents are failing to index, the moderator can pause the crew's operations. It separates the work from the management. You define the roles, and GroundX provides the data visibility they need to stay on track without your manual intervention.

Scalable memory via buckets

Use `list_content` and `list_buckets` to give your crew a persistent memory of all processed information. Your agents can reference previous findings by searching their own internal GroundX database. It functions as a shared knowledge base. When a new agent joins the crew, it can immediately query the existing buckets to get up to speed on the project history.

Setup guide

Set up GroundX 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 GroundX tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You pass the GroundX URL to your agent's `mcps` parameter. This makes tools like `search_documents` available to that specific agent immediately.

Yes, they share access to the same buckets. This allows your research agent to ingest documents while your analyst agent searches them at the same time.

You use a tool filter in your MCP server configuration. This ensures that only the agents who need to modify data can access tools like `create_bucket`.

The server manages parallel requests efficiently. Each agent gets its own session context, so searches don't interfere with one another.

Your data is stored in isolated containers that are invisible to outside actors. Your agent crew interacts only with the tools you provide, maintaining strict data privacy.

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