How to Use the Tingg Insights MCP in CrewAI

Q: How do I configure CrewAI to use Tingg Insights?

Install the package with pip install crewai "crewaitools". You pass the HTTP endpoint directly to your agent using the mcps array parameter.

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

Yes. Import MCPServerHTTP from crewai.mcp and use the toolfilter argument. This lets you give one agent read-only access and another agent write access.

Q: How do I automate bulk transfers?

Assign an execution agent to run the initiatepayoutrequest tool. You feed it a CSV of recipients, and the agent processes the entire batch sequentially.

Deploy a team of autonomous fintech agents by connecting Tingg Insights to CrewAI.

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Setup Tingg Insights with CrewAI

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Monitor payment flows autonomously

Assign a dedicated monitor agent to run `list_payment_transactions` every five minutes. The agent builds a baseline of normal activity across East African markets and watches for deviations. When the monitor detects an anomaly, it passes the exact ID to an investigator agent. The investigator fires `get_transaction_status` to determine if the failure was a network timeout or insufficient funds.

Process refunds via this MCP Server

Build a hierarchical crew to execute `initiate_payment_refund` autonomously using this MCP Server. A frontline agent receives the support ticket and verifies the user's claim against the database. If the claim is valid, it escalates to a moderator agent. The moderator executes the refund and updates the ticket, resolving the issue without human intervention.

Detect outages and page on-call engineers

Keep your infrastructure honest by having a background agent check `check_api_health` continuously. If the gateway goes down, you don't wait for a customer to complain. The agent immediately triggers `send_engagement_notification` to page the on-call engineer. The crew handles the detection and the escalation entirely on its own.

Setup guide

Set up Tingg Insights 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 Tingg Insights tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Install the package with `pip install crewai "crewai[tools]"`. You pass the HTTP endpoint directly to your agent using the `mcps` array parameter.

Yes. Import `MCPServerHTTP` from `crewai.mcp` and use the `tool_filter` argument. This lets you give one agent read-only access and another agent write access.

The framework supports standard stdio, Server-Sent Events (SSE), and Streamable HTTP. CrewAI negotiates the connection automatically.

Assign an execution agent to run the `initiate_payout_request` tool. You feed it a CSV of recipients, and the agent processes the entire batch sequentially.

The server runs in an ephemeral sandbox that never logs your SMS notification targets or payment recipient numbers. CrewAI agents process the context in memory, and the MCP bridge discards the payload immediately after execution.

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