How to Use the Option Pool Shuffle Simulator MCP in Pydantic AI

Build strictly typed dilution models using the Option Pool Shuffle Simulator and Pydantic AI.

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Connect Option Pool Shuffle Simulator MCP to Pydantic AI

Create your Vinkius account to connect Option Pool Shuffle Simulator 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 Option Pool Shuffle Simulator with Pydantic AI

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Type-safe pre-money calculations

Finance math requires absolute precision. Silent failures in cap table calculations destroy trust in your application. Executing `compute_pre_money_scenario` calculates the exact founder hit from an investor-friendly pool. Pydantic AI validates the returned floats at runtime so your agent never hallucinates a wildly incorrect equity percentage.

Validate post-money outcomes

Moving the option pool to the post-money side changes the entire math structure. Your system must handle these alternative calculations without dropping decimal points. The `compute_post_money_scenario` function maps out this founder-friendly setup. If the API returns a string instead of a number, the framework throws a loud validation error immediately.

Compare term sheets via MCP Server

Showing the gap between two term sheets requires a strict data schema. You need guaranteed fields for the dollar difference and percentage changes. Running `compare_shuffle_impact` quantifies the specific cost of the investor shuffle trick. You define the exact Pydantic model you expect back, and the agent enforces it across any LLM you choose.

Setup guide

Set up Option Pool Shuffle Simulator 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": {
        "option-pool-shuffle-simulator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Option Pool Shuffle Simulator transactions")
print(result.output)

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Common questions about Option Pool Shuffle Simulator MCP in Pydantic AI

Install the slim package with MCP support. Create an MCPToolset pointing to your Vinkius HTTP endpoint. Pass this toolset directly to your Agent instance.

Yes. The framework is completely model-agnostic. You can run the dilution math through an open-source model just as easily as a commercial one.

You likely received a validation error. The framework catches unexpected data types from the MCP immediately. Check your defined Pydantic models against the actual JSON response.

No. That class is deprecated in the latest release. Always use the unified MCPToolset approach for external HTTP endpoints.

The server only sees the numerical inputs like investment size and target pool percentage. Your proprietary cap table spreadsheets stay secure on your local machine. The MCP performs the math blindly.

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