How to Use the Armano MCP in CrewAI

Q: Can I restrict which Armano tools a CrewAI agent uses?

Yes. Import MCPServerHTTP from crewai.mcp and use the toolfilter parameter. This prevents your junior research agent from accidentally calling createtimeentry.

Q: How do CrewAI agents share Armano project data?

Agents operate with shared memory. If your researcher runs getproject, the analysis agent can read those exact details from the context window to write its final report.

Deploy autonomous CrewAI agents to monitor projects and audit employee time entries.

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Connect Armano MCP to CrewAI

Create your Vinkius account to connect Armano 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 Armano with CrewAI

Ask AI about this MCP

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Claude

Perplexity

Assign Armano tasks to CrewAI agents

One bot cannot run an entire HR department. You need a team. Setting up this MCP Server lets you assign specific company data access to different specialized workers in your crew. You give the auditor agent access to `list_time_entries` and `get_time_entry`. Meanwhile, your reporting agent only gets `list_projects`. They share memory and pass data back and forth to compile weekly summaries without human input.

Audit leave requests autonomously

Tracking who took time off is tedious. Hand that job over to a dedicated background process. A monitor agent watches the schedules while an execution agent fixes discrepancies. The crew pulls `list_leaves` to see approved vacations. It cross-references that with `list_employees` to confirm department policies. If someone logged regular hours while on PTO, the agent flags it for review.

Map organizational structures

Restructuring a company means updating dozens of spreadsheets. Let a research agent do the heavy lifting. It maps out reporting lines and project assignments automatically. The agent starts with `list_departments` to get the lay of the land. Then it iterates through `get_employee` to map out who reports to whom. It compiles a complete organizational snapshot in minutes.

Setup guide

Set up Armano 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 Armano tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You define your agent and drop the Vinkius endpoint right into the `mcps` array. CrewAI automatically discovers the available tools and equips the agent with them.

Yes. Import `MCPServerHTTP` from `crewai.mcp` and use the `tool_filter` parameter. This prevents your junior research agent from accidentally calling `create_time_entry`.

Agents operate with shared memory. If your researcher runs `get_project`, the analysis agent can read those exact details from the context window to write its final report.

It fits perfectly into a manager-worker setup. A manager agent can review the output of `check_armano_status` and delegate specific data gathering tasks to subordinate agents.

Vinkius hosts the connection behind strict ephemeral sandboxing. When your crew reads time entries or leave requests, the data passes through a secure pipe that instantly self-destructs after the session ends.

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