How to Use the Massive MCP in Pydantic AI

Q: What happens if Massive returns an invalid ticker error in Pydantic AI?

The listdividends tool raises a structured error that your agent catches as a validation failure. This allows your code to handle typos in stock tickers gracefully without crashing the entire run.

Fetch validated historical dividend records directly into your Pydantic AI agent workflows via our MCP Server.

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Connect Massive MCP to Pydantic AI

Create your Vinkius account to connect Massive 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 Massive with Pydantic AI

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Type-safe dividend tracking with Pydantic AI

The `list_dividends` tool brings strict runtime validation to your financial Pydantic AI agent workflows by checking every payout record against defined schemas. This ensures your Pydantic AI agent never processes malformed dividend dates or corrupted cash amounts. If the data source returns unexpected formats, the Pydantic AI system raises a validation error immediately. You catch data discrepancies at runtime before they pollute your portfolio calculations.

Prevent hallucinated payouts via strict MCP Server schemas

The `list_dividends` tool enforces rigid data structures for every historical record it retrieves, eliminating the risk of your Pydantic AI agent hallucinating dividend numbers on this MCP Server. Instead of guessing payout patterns, the Pydantic AI agent relies on verified records. Your financial reports remain mathematically accurate because the Pydantic AI agent only works with verified, structured data. You get reliable yield calculations without writing complex custom parsing logic.

Integrate dividend lookups using unified toolsets

You connect the `list_dividends` tool using the unified toolset configuration, which handles the underlying HTTP transport automatically inside Pydantic AI. This keeps your Pydantic AI agent code clean and free of deprecated connection methods. The toolset exposes the dividend lookup function directly to your model-agnostic Pydantic AI agent. Swapping models no longer requires rewriting your financial data fetching logic.

Setup guide

Set up Massive 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": {
        "massive-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Massive MCP in Pydantic AI

Install the MCP extension, initialize the unified toolset with your server's HTTP endpoint, and pass it to your agent's toolsets parameter. The agent will automatically detect and validate the `list_dividends` tool.

Yes, you can connect using either streamable HTTP or SSE transports depending on your network setup. The toolset handles the connection details so your agent can focus on querying dividend records.

The `list_dividends` tool raises a structured error that your agent catches as a validation failure. This allows your code to handle typos in stock tickers gracefully without crashing the entire run.

Yes, because the toolset is model-agnostic, you can run dividend lookups using local models or commercial APIs. The validation rules remain exactly the same regardless of which LLM you choose.

Your ticker queries and the resulting historical dividend records are never cached or inspected. We run every request through an ephemeral sandbox that completely isolates your financial queries from other users.

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