How to Use the CustomerGauge MCP in CrewAI

Q: How do I restrict which CustomerGauge MCP tools a CrewAI agent can use?

You use the toolfilter parameter during setup. This lets you give one agent read-only access while another gets permission to execute complex searches.

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Connect CustomerGauge MCP to CrewAI

Create your Vinkius account to connect CustomerGauge 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 CustomerGauge with CrewAI

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Perplexity

Assign CustomerGauge tools to CrewAI agents

Stop writing monolithic scripts. You can assign the `get_portfolio_nps_summary` tool from this MCP Server directly to a dedicated Research Agent. This worker wakes up, checks global experience metrics, and logs its findings into shared memory. The next agent takes over automatically. Your Analyst Agent reads that shared memory and decides to fire off `get_business_unit_nps` to find exactly which department caused the dip. They work together without you writing the logic.

Investigate specific survey feedback autonomously

This MCP integration lets one agent handle the broad strokes while another digs into the details. A Monitor Agent runs `search_responses_by_keyword` to look for mentions of pricing or bugs in the recent feedback. When it finds a match, it hands the response ID to a deep-dive agent. That second worker uses `get_response_details` to pull the verbatim comments and driver scores. You just sit back and read the final report.

Map revenue risk to account contacts

Tying financial data to human relationships takes too much manual clicking. Your Financial Agent can pull monetary values using `list_revenue_impact_data` and identify which high-value accounts are at risk. A Relationship Agent then takes that list and runs `list_account_contacts`. It builds a complete dossier of who to call, matching contact identifiers with the exact revenue at stake. The crew handles the entire investigation.

Setup guide

Set up CustomerGauge 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 CustomerGauge tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass your Vinkius endpoint directly into the agent setup array. The framework automatically parses the available operations and equips your worker with them.

Yes. One worker can fetch the NPS scores while another analyzes them. They pass the context through their shared memory pool automatically.

You use the tool_filter parameter during setup. This lets you give one agent read-only access while another gets permission to execute complex searches.

It relies on a manager agent to delegate the work. The manager looks at the goal, decides which worker needs to run the API call, and evaluates the result before moving on.

We built the infrastructure on a zero-trust model. Your organizational mappings and contact identifiers only exist in memory during the task execution. The MCP Server sandbox terminates immediately after.

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