How to Use the Axiom MCP in CrewAI

Q: Can I restrict which tools a CrewAI agent can use?

Yes, use the MCP server class from crewai.mcp and apply a toolfilter. This lets you prevent a junior agent from executing destructive actions like deletedataset.

Deploy a crew of specialized AI agents to analyze Axiom logs, manage monitors, and coordinate incident response with CrewAI.

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Connect Axiom MCP to CrewAI

Create your Vinkius account to connect Axiom 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 Axiom with CrewAI

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Multi-agent log analysis teams

The `run_query` tool lets your CrewAI analyst agents scan raw datasets for security anomalies or performance bottlenecks. One agent can write and execute APL queries while a second agent evaluates the results. This cooperative execution allows for deep, multi-layered log investigations. If the analyst agent finds an issue, it passes the context to a coordinator agent to quickly isolate the affected services.

Autonomous incident escalation

The `create_monitor` tool enables your CrewAI operations team to establish new watchdogs when fresh code deploys. A specialized monitoring agent continuously checks system behavior and sets up alerts automatically. When an alert fires, the crew runs `list_notifiers` to find the correct on-call channel. They immediately route the diagnostic data to Slack or PagerDuty, cutting down response times during critical outages.

Smart dataset maintenance via MCP Server

The `list_datasets` tool allows your CrewAI team to audit storage usage and clean up abandoned staging environments. A dedicated maintenance agent reviews active datasets to identify unused resources. After compiling the inactive list, the agent coordinates with a supervisor agent to run `delete_dataset` safely. This keeps your observability billing in check without requiring manual database auditing.

Setup guide

Set up Axiom 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 Axiom tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass your Vinkius MCP endpoint URL directly into the agent's `mcps` array. CrewAI automatically discovers the tools and exposes them to your specialized agents.

Yes, use the MCP server class from `crewai.mcp` and apply a `tool_filter`. This lets you prevent a junior agent from executing destructive actions like `delete_dataset`.

CrewAI's shared memory system allows an analyst agent running `run_query` to pass the raw log outputs directly to a reporter agent. They collaborate on the data without re-running the query.

The framework supports stdio, SSE, and Streamable HTTP. For managed Vinkius hosting, you will typically configure the SSE or HTTP transport using your secure endpoint.

Absolutely. Your log datasets, query responses, and API tokens are never exposed to external model providers. Vinkius runs the execution environment inside ephemeral, zero-trust sandboxes to guarantee data isolation.

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