How to Use the AppVeyor MCP in CrewAI

Q: How do I prevent a CrewAI agent from accidentally deleting an AppVeyor project?

Use CrewAI's advanced setup with MCPServerHTTP and a toolfilter. You can explicitly exclude deleteproject and deleteuser from the tools exposed to the agent, ensuring it only has read and build-trigger capabilities.

Deploy a crew of autonomous agents to monitor, triage, and manage your AppVeyor pipelines using CrewAI.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect AppVeyor MCP to CrewAI

Create your Vinkius account to connect AppVeyor 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.

GDPR Free for Subscribers

Setup AppVeyor with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-Agent Build Triage Teams

The `get_project_history` tool gives your CrewAI monitoring agent the raw data needed to spot AppVeyor build patterns. When an AppVeyor build fails, the CrewAI monitoring agent passes the project details to a developer agent. The CrewAI developer agent uses `get_project_settings` to analyze the AppVeyor configuration. It then decides whether to run `rerun_build` on AppVeyor or escalate the issue to a human engineer over Slack.

Automated Security Audits via CrewAI

This MCP Server enables your CrewAI compliance agent to run `list_collaborators` to verify who has access to your AppVeyor build environments. The CrewAI agent compares this list against your company registry to find unauthorized AppVeyor users. If it finds an anomaly, a CrewAI moderator agent calls `delete_collaborator` or updates roles using `update_role` on AppVeyor. Automating this process keeps your AppVeyor continuous integration environment secure without manual oversight.

Multi-Agent Environment Management

The `list_environments` tool allows your CrewAI infrastructure agent to check all active AppVeyor deployment targets. It coordinates with an auditing agent to ensure AppVeyor settings match your production requirements. When a drift is detected, the CrewAI agent uses `get_environment_settings` to pinpoint the AppVeyor discrepancies. The CrewAI crew then documents the differences or updates the AppVeyor target environments to restore consistency.

Setup guide

Set up AppVeyor 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 AppVeyor tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent AppVeyor 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 AppVeyor. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect AppVeyor now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about AppVeyor MCP in CrewAI

Pass your Vinkius MCP URL directly into the agent's mcps array. This gives that specific agent immediate access to tools like start_build and list_projects while keeping other agents scoped to their own tasks.

Yes, CrewAI excels at this. Your Orchestrator agent can run start_build, then pass the build ID to a QA agent that polls get_project_last_build until the deployment succeeds or fails.

Use CrewAI's advanced setup with MCPServerHTTP and a tool_filter. You can explicitly exclude delete_project and delete_user from the tools exposed to the agent, ensuring it only has read and build-trigger capabilities.

Agents utilize CrewAI's shared memory. When the deployment agent runs start_deployment, it writes the deployment status to the shared context so the monitoring agent knows exactly when to start tracking the run.

The CrewAI framework only interacts with the AppVeyor MCP Server via secure, ephemeral Vinkius endpoints. Your sensitive collaborator IDs and build tokens are never stored or exposed within the CrewAI agent memory logs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript