How to Use the DataScope MCP in CrewAI

Q: Can a CrewAI agent filter DataScope submissions by user?

Yes, your agents can use listformsubmissions and pass a specific user ID as a parameter. This lets a specialized auditor agent focus on a single team member's reports.

Q: What python packages do I need?

You need to install both crewai and its tools extension using pip. Run pip install crewai "crewaitools" to get the necessary libraries for connecting to external servers.

Run autonomous multi-agent crews to audit and analyze DataScope field reports with CrewAI.

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Connect DataScope MCP to CrewAI

Create your Vinkius account to connect DataScope 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 DataScope with CrewAI

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Run multi-agent audits on DataScope submissions

The `get_submissions_with_metadata` tool delivers detailed field audit logs directly to your CrewAI agents. One agent can pull the raw submission details while a second agent analyzes the data for compliance issues. You configure this by passing the MCP endpoint URL inside the `mcps` array in your agent definition. This lets your specialized crew collaborate on offline data syncs without manual intervention.

Map field inspectors to active zones using CrewAI

The `list_tracked_locations` tool provides real-time geographic data for your field agents. Your CrewAI coordinator agent can use these locations to assign nearby inspection tasks to available workers. By using the `MCPServerHTTP` wrapper, you can restrict which MCP tools are exposed to specific agents. This ensures your mapping agent only receives location data, keeping its focus sharp.

Generate automated PDF reports with an MCP Server

The `get_submission_pdf_url` tool fetches the direct link to the generated PDF for any completed inspection. This allows a writer agent in your crew to compile these links into a daily summary email for stakeholders. The crew runs sequentially, ensuring the PDF is retrieved only after the metadata analyzer agent verifies the submission. This workflow runs entirely unattended in your Python environment.

Setup guide

Set up DataScope 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 DataScope tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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="DataScope Analyst",
        goal="Access and analyze DataScope data via MCP.",
        backstory="Expert analyst with direct DataScope access.",
        tools=mcp_tools,
    )

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

The easiest way is to pass the server's endpoint URL directly into the `mcps` list of your Agent definition. For more granular control, use `MCPServerHTTP` to filter specific tools.

Yes, your agents can use `list_form_submissions` and pass a specific user ID as a parameter. This lets a specialized auditor agent focus on a single team member's reports.

Agents share a memory space, allowing them to pass the results of `list_available_forms` to one another. One agent identifies the active templates while another pulls the corresponding submissions.

You need to install both `crewai` and its tools extension using pip. Run `pip install crewai "crewai[tools]"` to get the necessary libraries for connecting to external servers.

GPS coordinates and location histories fetched from field devices are transmitted directly to your local Python runtime. No location tracking data is stored on external logging servers, maintaining strict operational privacy.

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