How to Use the CTO Architect Prover MCP in CrewAI

Deploy autonomous architecture squads with CrewAI and the CTO Architect Prover to enforce security and stack fitness automatically.

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…and any MCP-compatible client

MCP Servers — Included with Plan

Connect CTO Architect Prover MCP to CrewAI

Create your Vinkius account to connect CTO Architect Prover to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup CTO Architect Prover with CrewAI

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Automated security hardening in CrewAI

Stop relying on generic security statements. The `validate_cto_architect` tool forces your agents to define specific security controls, such as exact token types and whitelist-only CORS policies. It flags any architecture that lacks parameterized queries or proper key rotation. Your agents get the hard constraints they need to build secure systems.

Observability validation for CrewAI crews

Ensure your autonomous agents build systems that can actually be debugged. This MCP Server requires explicit health probes and alert thresholds for every component. It rejects any plan that relies on 'monitoring' without concrete metrics. Your agents must prove they have log aggregation and recovery procedures ready.

Stack fitness rigor for CrewAI agents

Stop your agents from picking technologies just because they are popular. The `validate_cto_architect` tool forces a comparison between the chosen stack and your team's actual capacity. It prevents resume-driven development by rejecting overly complex systems that fail to meet your specific budget and timeline constraints.

Setup guide

Set up CTO Architect Prover 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 CTO Architect Prover tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent CTO Architect Prover 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 →

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