How to Use the Fig Finance MCP in Pydantic AI

Q: How do I integrate the Fig Finance MCP Server with Pydantic AI?

Use the MCPToolset constructor with the Fig Finance server URL and pass it to your Pydantic AI agent. The framework automatically maps Fig Finance tools like getloanstatus and listcustomers into type-safe Python methods.

Q: What happens if Fig Finance returns an invalid repayment schedule to Pydantic AI?

If getrepayments returns data that violates your Pydantic AI schema, the framework raises a validation error instantly. This prevents your Pydantic AI agent from writing corrupted Fig Finance schedule calculations to your database.

Q: How does Pydantic AI secure my Fig Finance repayment schedules and loan applications?

Every Pydantic AI call to getrepayments and applyforloan is routed through a secure, single-token connection managed by Vinkius. Your Fig Finance data is parsed in memory and validated locally by Pydantic AI, ensuring no raw loan application details are leaked to external logs.

Validate Fig Finance loan offers at runtime using Pydantic AI to prevent corrupt data and bad disbursements.

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Connect Fig Finance MCP to Pydantic AI

Create your Vinkius account to connect Fig Finance to Pydantic AI 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 Fig Finance with Pydantic AI

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Strict Type Validation for Fig Finance

In financial operations, silent failures are dangerous, which is why Pydantic AI forces every response from the Fig Finance MCP server to match your strict Python schemas at runtime. If `get_loan_offers` returns an unexpected data structure or an invalid interest rate, your Pydantic AI agent fails immediately instead of processing a corrupt Fig Finance offer. This strict validation applies to all critical Pydantic AI actions, including `apply_for_loan`. By catching schema mismatches before executing Fig Finance API calls, you ensure your lending operations remain completely compliant.

Type-Safe Customer Onboarding in Pydantic AI

Onboarding borrowers requires clean data, so your Pydantic AI agent uses this MCP integration to run `create_customer` in Fig Finance, validating their profiles against your internal models before sending the request. The Pydantic AI agent then verifies the registration using `get_customer` to ensure all fields match your Fig Finance rules. If a customer's profile is incomplete, the Pydantic AI system raises a validation error before any Fig Finance API call is made. This keeps your Fig Finance database clean and prevents your Pydantic AI agent from attempting to process loans with broken records.

Guaranteed Ledger Auditing and Disbursements

When moving money, you can't afford hallucinations, so your Pydantic AI agent verifies the exact balance using `get_balance` before triggering Fig Finance `disburse_funds`. Every transaction record returned by `list_transactions` is parsed into strongly-typed Pydantic AI models, guaranteeing your Fig Finance ledger balances perfectly. By enforcing Pydantic AI type safety on `get_repayments` and `list_loans`, you completely eliminate the risk of an LLM misinterpreting Fig Finance dates or currency values. Your automated Fig Finance pipeline runs with the same mathematical rigor as traditional banking software.

Setup guide

Set up Fig Finance 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": {
        "fig-finance-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Fig Finance transactions")
print(result.output)

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Common questions about Fig Finance MCP in Pydantic AI

Use the `MCPToolset` constructor with the Fig Finance server URL and pass it to your Pydantic AI agent. The framework automatically maps Fig Finance tools like `get_loan_status` and `list_customers` into type-safe Python methods.

If `get_repayments` returns data that violates your Pydantic AI schema, the framework raises a validation error instantly. This prevents your Pydantic AI agent from writing corrupted Fig Finance schedule calculations to your database.

Yes, Pydantic AI is model-agnostic, allowing you to use OpenAI, Anthropic, or local models to run your Fig Finance workflows. This ensures that tools like `get_me` work consistently regardless of your LLM provider.

Yes, the Fig Finance MCP server runs externally, and your Pydantic AI agent connects to it via streamable HTTP or SSE transports using the unified `MCPToolset` interface.

Every Pydantic AI call to `get_repayments` and `apply_for_loan` is routed through a secure, single-token connection managed by Vinkius. Your Fig Finance data is parsed in memory and validated locally by Pydantic AI, ensuring no raw loan application details are leaked to external logs.

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