How to Use the Aporia MCP in CrewAI

Q: How do I configure CrewAI to use the Aporia MCP Server?

Pass the endpoint URL directly into the mcps array when defining your Agent. For more granular control over which agent gets which tool from the Aporia MCP server, import MCPServerHTTP and apply a toolfilter.

Deploy autonomous security teams in CrewAI. Equip your moderator agents with Aporia tools to police other agents in real-time.

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Connect Aporia MCP to CrewAI

Create your Vinkius account to connect Aporia 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 Aporia with CrewAI

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Claude

Perplexity

Inter-Agent Safety Checks

`validate_guardrails` gives your moderator agent the ability to inspect outputs generated by your research agents. You pass the conversation history to the Aporia MCP server, and it flags off-topic tangents or toxic language before the final report compiles. The crew polices itself. If the guardrail trips, the moderator agent rejects the draft and forces the writer agent to revise the content. You build a self-correcting loop that requires zero human intervention.

CrewAI Fleet Observability

`list_models` and `list_monitors` allow a dedicated audit agent to map your entire observability coverage. The agent queries the workspace to verify that every active model has a corresponding safety rule attached. Missing monitors get caught immediately. The audit agent can cross-reference the active configurations and alert your SRE team if a new model deploys without proper guardrails in place.

Trigger Diagnostics on Demand

`trigger_monitor` and `get_metrics` let your diagnostic agents investigate performance degradation mid-task. When a worker agent experiences high latency, the diagnostic agent pulls the drift metrics to find the bottleneck. You stop relying on passive dashboards. The crew actively queries the MCP Server for real-time telemetry, adjusting their own execution hierarchies based on the live health of the underlying LLMs.

Setup guide

Set up Aporia 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 Aporia tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the endpoint URL directly into the `mcps` array when defining your Agent. For more granular control over which agent gets which tool from the Aporia MCP server, import `MCPServerHTTP` and apply a `tool_filter`.

Yes. You assign the validation tools exclusively to a moderator role. The writer passes its output to the moderator, who executes the check before handing the data back to the manager.

You configure your specific rules inside the workspace first. The agents then evaluate their messages against those predefined boundaries, ensuring corporate compliance across the entire crew.

Assign the metrics tools to an analyst agent. That agent requests the performance data, interprets the drift values, and writes a summary report detailing the health of your infrastructure.

The server receives the system prompts and agent responses solely for boundary testing. Aporia scans the conversation array for toxicity or PII, returns the pass/fail boolean, and immediately flushes the memory. The internal logic remains completely secure.

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