How to Use the Gainsight CS MCP in CrewAI

Q: How do I give my CrewAI agents access to the Gainsight CS MCP Server?

Install the crewaitools package. Then add the server URL directly to the mcps array in your agent definition.

Q: Can I restrict certain CrewAI agents from writing to Gainsight CS?

Yes. Use the MCPServerHTTP class with toolfilter to hide the writing tools from your research agents.

Deploy a CrewAI agent team to autonomously monitor Gainsight CS MCP Server health scores and execute playbook tasks.

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Connect Gainsight CS MCP to CrewAI

Create your Vinkius account to connect Gainsight CS 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 Gainsight CS with CrewAI

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Autonomous Health Monitoring

The `get_company_health` tool gives your monitor agent direct access to account status metrics. This agent constantly scans the portfolio by cross-referencing `list_customer_companies`. When a score drops below your threshold, it alerts the rest of the crew. You set this up as a background operation requiring zero human input. The monitor passes the flagged accounts into shared memory so the response agents know exactly who needs attention.

Collaborative Task Execution

Your execution agent pulls the daily workload using the `list_cockpit_tasks` tool. It reads the specific instructions for each item via `get_task_metadata` and determines the priority order. The crew handles the routine follow-ups entirely on its own. You pass the server URL directly into the mcps array to give the agents access. For tighter control, you can restrict which agent sees which endpoints using a tool filter.

CrewAI MCP Server Timeline Management

This MCP Server allows your documentation agent to write meeting summaries using the `log_timeline_activity` tool. Another agent can audit previous interactions by calling `list_timeline_events`. They work together to maintain a perfect historical record. You run these agents in sequential or hierarchical patterns depending on your specific playbook. They communicate through standard stdio or SSE transports without any extra configuration.

Setup guide

Set up Gainsight CS 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 Gainsight CS tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Install the `crewai[tools]` package. Then add the server URL directly to the `mcps` array in your agent definition.

Yes. Use the `MCPServerHTTP` class with `tool_filter` to hide the writing tools from your research agents.

Vinkius handles the underlying auth. You just provide your single endpoint token when initializing the connection.

Yes. Agents can call the reading tools concurrently and share the resulting data in the crew's shared memory.

The agents process direct customer health metrics and timeline events. Vinkius routes this traffic through an ephemeral, zero-trust sandbox that leaves no trace.

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