How to Use the Celoxis MCP in CrewAI

Deploy autonomous CrewAI agent teams to monitor Celoxis portfolios and resolve project risks without human intervention.

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Connect Celoxis MCP to CrewAI

Create your Vinkius account to connect Celoxis 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 Celoxis with CrewAI

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CrewAI Portfolio Monitoring Teams

Assigning `list_portfolios` and `list_projects` gives your monitor agent a global view of all top-level aggregates. CrewAI excels at assigning specific roles to different LLM instances. One agent watches the timeline data while another looks for status anomalies. Investigating specific flags requires the `get_project` MCP tool to pull complete intrinsic properties. A researcher agent grabs this physical ID data and passes it to an analyst. Shared memory ensures the entire crew understands the context before taking action.

Autonomous Resource Allocation

Feeding `list_resources` and `list_tasks` to a specialized planner agent allows it to parse core user mappings. Hierarchical execution means a manager agent can evaluate the WBS deliverables before assigning them. Workloads balance themselves based on actual availability. Monitoring absolute phase delivery natively happens via `list_milestones`. Should a phase fall behind, the crew can identify the bottleneck automatically. Nobody has to manually check the schedule anymore.

Audit Expenses with AI Crews

Exposing `list_expenses` and `list_time_entries` creates a perfect environment for an MCP auditor agent. Bots specifically look at actual time logged explicitly against tasks for accounting discrepancies. Suspicious entries get flagged and sent to a moderator. Reviewing `list_approvals` lets the crew verify pending and cleared constraints across the entire organization. Setup the sequential process once and let the system run on a cron job. Oversight shifts from a manual chore to a background process.

Setup guide

Set up Celoxis 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 Celoxis tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pip install the tools package directly into your Python environment. Pass the Vinkius MCP endpoint URL into your agent's configuration array. Advanced setups can use the HTTP server class for selective exposure.

Role-based specialization makes this easy. Scouts find the organizational risks while a strategist formulates a mitigation plan. They hand off data directly.

Standard IO, SSE, and Streamable HTTP are all available. Defaults favor HTTP for remote connections like Vinkius. Pick the protocol that fits your infrastructure.

Agents remember the explicit CRM clients and portfolios they already queried. Future prompts reference this historical context automatically. Duplicate API calls drop significantly as the crew learns the project state.

Safeguarding your working resources and WBS tasks is our top priority. Authentication tokens grant access to exactly one sandboxed environment. Only isolated sessions exist, dropping all data after execution.

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