How to Use the YC Startup Prover MCP in Pydantic AI

Catch bad business models with type-safe runtime validation using the YC Startup Prover MCP Server in Pydantic AI.

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MCP Servers — Included with Plan

Connect YC Startup Prover MCP to Pydantic AI

Create your Vinkius account to connect YC Startup Prover 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 YC Startup Prover with Pydantic AI

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Enforce strict type-safe pitch audits in Pydantic AI

If your agent evaluates a startup, you cannot afford silent errors or hallucinated metrics. This MCP Server integrates directly with your type-safe pipelines to ensure every audit response conforms strictly to your schemas. Calling `validate_yc_startup` guarantees that your agent receives structured, validated data. If the server detects hand-waving instead of concrete user feedback, the agent fails loudly with a validation error rather than passing bad data forward.

Avoid silent failures during startup business model analysis

Most frameworks let agents hallucinate missing metrics when evaluating a pitch. Pydantic AI prevents this by validating every field against strict Python models at runtime, forcing the agent to verify actual weekly retention curves. This strictness ensures your system catches premature scaling and feature bloat before writing any summary reports. You get clean, predictable outputs every single run.

Connect type-safe agents via HTTP or SSE transports

You can run this server externally and connect your agents using streamable HTTP or Server-Sent Events. The unified MCP toolset interface makes integration straightforward without dealing with deprecated connection classes. This setup keeps your runtime lightweight while offloading the heavy business logic to a secure, isolated sandbox. Your agent gets fast, reliable validation with minimal boilerplate.

Setup guide

Set up YC Startup Prover 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": {
        "yc-startup-prover-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent YC Startup Prover transactions")
print(result.output)

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Common questions about YC Startup Prover MCP in Pydantic AI

Use the unified toolset class initialized with your server's HTTP address. Pass the toolset instance into your agent constructor's toolsets parameter to enable validation.

Yes. Every evaluation response is validated against strict Pydantic models at runtime, ensuring your agent never processes malformed or hallucinated startup metrics.

You can connect using either Streamable HTTP or Server-Sent Events. The unified toolset interface handles both transports natively under the hood.

Yes, you should use the unified toolset class instead of the deprecated HTTP server class. This ensures compatibility with the latest validation updates.

Your pitch documents and user feedback data are processed entirely in memory within an ephemeral V8 sandbox. No data is stored, written to disk, or used for model training.

Use it with your favorite AI tools

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