How to Use the Albi Restoration MCP in CrewAI

Q: Can I restrict which Albi Restoration tools my CrewAI agents see?

Yes, use the MCPServerHTTP class from the MCP module. You apply a toolfilter to only expose specific endpoints. Keep your analyst agent away from the creation endpoints.

Q: How do agents know who to assign to an Albi Restoration project in CrewAI?

Your setup crew calls listdefinedprojectroles during initialization. They store this hierarchy in their shared memory. Subsequent agents reference this list when drafting assignment proposals.

Deploy specialized multi-agent teams in CrewAI to autonomously monitor and update your Albi Restoration projects.

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Create your Vinkius account to connect Albi Restoration 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 Albi Restoration with CrewAI

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Delegate Financial Audits

`get_project_financial_kpis` feeds raw margin and cost data directly to your designated analyst agent. CrewAI allows you to assign specific roles, so one agent focuses entirely on the numbers. It pulls the metrics, compares them against historical averages, and writes a summary. Meanwhile, a researcher agent runs `get_project_details` to pull the context behind those numbers. They share memory automatically. The final output combines the hard financials with the operational realities on the ground.

CrewAI Meets MCP Server

`list_restoration_organizations` hands your account management crew a complete view of your client base. You set up a sequential process where Agent A maps out the organizations. Agent B then groups them by region. Agent C takes that list and queries `list_restoration_projects` for every matched company. The entire operation runs without human input. Your team wakes up to a fully formatted territory report.

Organize Field Operations

`list_project_activities` acts as the dispatch board for your coordinator agent. It reads the daily tasks and identifies which jobs are falling behind schedule. The agent flags high-priority items based on the criteria you define. To fix the delays, a secondary agent checks `list_inventory_equipment` to see if missing gear is the problem. It cross-references asset locations with job sites. The system spits out a concrete redistribution plan.

Setup guide

Set up Albi Restoration 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 Albi Restoration tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Albi Restoration 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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Common questions about Albi Restoration MCP in CrewAI

Install the framework with the tools extra enabled via pip. Pass your Vinkius endpoint array directly into the `mcps` parameter on your agent definition. The framework handles the rest.

Yes, use the `MCPServerHTTP` class from the MCP module. You apply a `tool_filter` to only expose specific endpoints. Keep your analyst agent away from the creation endpoints.

Your setup crew calls `list_defined_project_roles` during initialization. They store this hierarchy in their shared memory. Subsequent agents reference this list when drafting assignment proposals.

The endpoint requires both a project name and a primary contact ID. If your agent skips the contact creation step, the API rejects the request. Force a sequential flow to guarantee the prerequisites exist first.

Every query executes within an isolated container that has no persistent storage. Your organization structures and equipment lists exist only in RAM during the transaction. Once the HTTP response completes, the execution environment terminates completely.

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