How to Use the YC Startup Prover MCP in LlamaIndex

Turn pitch evaluations into a searchable knowledge base. Connect YC Startup Prover to LlamaIndex and index startup viability.

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MCP Servers — Included with Plan

Connect YC Startup Prover MCP to LlamaIndex

Create your Vinkius account to connect YC Startup Prover to LlamaIndex — 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 LlamaIndex

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Index startup audits with LlamaIndex

Calling the `validate_yc_startup` MCP tool feeds Paul Graham's ruthless business logic straight into your indexing pipeline. It forces a strict breakdown of user obsession, core value, and metric discipline on any business document it processes. Instead of just reading a pitch deck once, your agent processes the evaluation and stores the outcome. You build a permanent, searchable record of why a specific idea failed the manual scaling test.

Query your past decisions

Founders love to pitch technology searching for a problem. When your system processes these pitches, the resulting critique becomes part of your vector store. Weeks later, you can ask your agent why you passed on that hardware startup. It pulls up the exact validation record showing they had zero user conversations and no willingness to pay.

Anchor evaluations in real metrics

Vanity metrics kill companies. This evaluation logic strips away Instagram followers and newsletter signups to isolate actual retention and revenue. Your RAG applications now have a built-in truth filter. When cross-referencing a new pitch against historical data, the system relies on hard numbers and proven problem discovery rather than founder optimism.

Setup guide

Set up YC Startup Prover MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all YC Startup Prover MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to YC Startup Prover tools.",
)
response = await agent.run("List recent YC Startup Prover data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by YC Startup Prover. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about YC Startup Prover MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate a BasicMCPClient. Convert the server connection using McpToolSpec and pass it to your FunctionAgent.

Yes. Once the tool processes a business plan, the structured output gets embedded into your vector store. You can search historical critiques instantly.

It standardizes the critique format. Your knowledge base fills up with uniform, five-axis evaluations instead of random, unstructured AI summaries.

The server evaluates whatever text your agent feeds it. Pass in local PDF transcripts or scraped website copy, and it returns the structured audit.

The server analyzes pitch decks, cap tables, and user interview transcripts inside an ephemeral sandbox. No data persists after the request, ensuring your proprietary deal flow remains strictly confidential.

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