How to Use the Travis CI MCP in CrewAI

Q: How does CrewAI use the MCP Server for multi-repo management on Travis CI?

CrewAI uses listtravisrepositories first. This gives the entire team a directory of all projects, allowing specialized agents to know exactly which repository slug they need to target.

Q: If an agent needs to run diagnostic tests on Travis CI, how is that initiated with CrewAI?

The 'Act' agent simply calls triggernewbuild, supplying the necessary repo slug and branch. The action happens automatically as part of the crew’s collaborative workflow.

Q: Can CrewAI monitor the build history across all branches on Travis CI?

Yes. They use listrepositorybranches to get a comprehensive view, which is crucial for determining if the current state of the codebase is ready for release or needs further investigation.

Q: How does the MCP Server handle identity verification when CrewAI interacts with Travis CI?

CrewAI uses getuserprofile to retrieve the authenticated user's details. This ensures that all operations initiated by the multi-agent system are correctly attributed and authorized.

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Setup Travis CI with CrewAI

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Map All Available Repositories

CrewAI can initiate a research phase by calling `list_travis_repositories` to map every project configured on Travis CI. The agents then use `get_repository_details` to gather initial metadata, like the organization ID and default branch status for each one. This allows the crew to build a complete understanding of the entire codebase landscape before any action is taken.

Execute Targeted Tests

To test a specific feature, one agent can call `trigger_new_build`, providing the exact repo slug, branch, and optional message. The crew then waits for the results to come back. If the initial run fails, another agent can use `restart_travis_build` or even `cancel_travis_build` to adjust the testing parameters autonomously.

Audit Build History and Users

The crew needs visibility. They check history using `list_repository_branches` to see the latest build status across all branches, helping them determine if a deployment is safe. They also run `get_user_profile` to verify which user context they are operating under. This auditing capability ensures that every action taken by the autonomous crew is logged and traceable.

Setup guide

Set up Travis CI 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 Travis CI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

CrewAI uses `list_travis_repositories` first. This gives the entire team a directory of all projects, allowing specialized agents to know exactly which repository slug they need to target.

The 'Act' agent simply calls `trigger_new_build`, supplying the necessary repo slug and branch. The action happens automatically as part of the crew’s collaborative workflow.

Yes. They use `list_repository_branches` to get a comprehensive view, which is crucial for determining if the current state of the codebase is ready for release or needs further investigation.

The server manages various identifying strings and object details, including repository slugs and user profiles. These are used to maintain context across different agent tasks.

CrewAI uses `get_user_profile` to retrieve the authenticated user's details. This ensures that all operations initiated by the multi-agent system are correctly attributed and authorized.

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