How to Use the Freightview MCP in CrewAI

Q: Can I restrict which Freightview tools my CrewAI agents use?

Yes. You should use MCPServerHTTP and configure a toolfilter. This lets you give the quoting agent access to requestrates while blocking it from seeing listwebhooks.

Q: How do multiple CrewAI agents share Freightview MCP data?

They use shared memory. If Agent A calls getaccountdetails, the resulting organization attributes are stored in the crew's context for Agent B to use later in the sequence.

Deploy autonomous logistics teams to manage your Freightview account using CrewAI agents.

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Connect Freightview MCP to CrewAI

Create your Vinkius account to connect Freightview 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 Freightview with CrewAI

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Build an Autonomous Quoting Crew via MCP

Stop running single agents. With CrewAI, you assign one agent to act as the logistics researcher and another as the financial auditor. They share memory and coordinate the entire freight booking process through this MCP Server. The researcher calls `list_item_catalog` to get weights and hits `request_rates` to pull options. The auditor then reviews the output from `list_freight_quotes` to verify the margins before passing the final choice to an execution agent.

Continuous Shipment Monitoring Teams

You can spin up a dedicated MCP tracking crew that runs in the background. A monitor agent constantly loops through `list_shipments` to watch for status changes across all your active loads. The moment a delivery flags as delayed via `get_shipment_details`, the monitor hands the context to a resolution agent. That agent uses `get_carrier_details` to find the exact terminal phone number and drafts an escalation report.

Audit Carrier Performance Automatically

Managing logistics relationships requires constant data analysis. You can task a CrewAI squad to evaluate your shipping history without human intervention. The team pulls historical data using `list_freight_quotes` and cross-references it with your saved facilities in `list_address_book`. They analyze which carriers consistently miss delivery windows and output a weekly performance summary.

Setup guide

Set up Freightview 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 Freightview tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the Vinkius endpoint directly into the mcps array in your Agent definition. CrewAI automatically fetches and registers all the available tools.

Yes. You should use MCPServerHTTP and configure a tool_filter. This lets you give the quoting agent access to request_rates while blocking it from seeing list_webhooks.

They use shared memory. If Agent A calls get_account_details, the resulting organization attributes are stored in the crew's context for Agent B to use later in the sequence.

You can connect via standard SSE or Streamable HTTP. CrewAI handles the underlying connection to the Vinkius managed infrastructure automatically.

Vinkius enforces strict data isolation. When your agents pull metadata via get_shipment_details, the sandbox processes the request and instantly terminates. No logs, no persistent storage.

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