How to Use the Tingg Insights MCP in Pydantic AI

Build type-safe African fintech agents with Pydantic AI and the Tingg Insights MCP Server.

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MCP Servers — Included with Plan

Connect Tingg Insights MCP to Pydantic AI

Create your Vinkius account to connect Tingg Insights to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Tingg Insights with Pydantic AI

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Type-safe checkout and refund operations with Pydantic AI

Your agent initiates customer payment flows using `create_checkout_request` and verifies the outcome via `get_transaction_status`. If a transaction fails or requires reversal, it runs `initiate_payment_refund` to return the funds. Checking network connectivity is handled using `check_api_health` before attempting any payment actions. This MCP setup validates every API response against strict schemas, preventing malformed data from corrupting your downstream systems.

Reliable payout management with Pydantic AI

The agent routes money to recipients using `initiate_payout_request` and tracks the transfer via `get_payout_status`. Verifying the complete disbursement history is handled by calling `list_disbursement_payouts`. If a mobile money carrier returns unexpected fields, the framework catches the validation error immediately. This prevents your agent from acting on corrupt or hallucinated payout data.

Structured settlement audits and performance metrics

The agent extracts financial performance stats using `get_account_performance_metrics` and reviews bank deposits with `list_account_settlements`. Daily revenue is tracked by auditing transactions via `list_payment_transactions`. Active event listeners are verified using `list_configured_webhooks` before sending updates with `send_engagement_notification`. This ensures your reporting pipelines receive clean, type-checked data for every payment event.

Setup guide

Set up Tingg Insights MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "tingg-insights-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Tingg Insights tools.",
)

result = await agent.run("List recent Tingg Insights transactions")
print(result.output)

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Common questions about Tingg Insights MCP in Pydantic AI

Install the MCP extension, then initialize MCPToolset with your Vinkius HTTP URL. Pass this toolset directly to your agent instance.

The framework raises a validation error immediately. This stops the agent from executing further steps with bad data.

Yes. The toolset handles async network calls natively, ensuring your agent remains responsive under heavy transaction loads.

Yes. The framework is model-agnostic, allowing you to use any compatible model to run your payment workflows.

The MCP Server handles customer phone numbers, bank details, and transaction histories. Vinkius processes these requests inside secure, ephemeral sandboxes, isolating your data from external storage.

Use it with your favorite AI tools

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