How to Use the Station F Global Prover MCP in LlamaIndex

Indexes global viability checks directly into LlamaIndex's knowledge base for RAG applications.

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Connect Station F Global Prover MCP to LlamaIndex

Create your Vinkius account to connect Station F Global 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 Station F Global Prover with LlamaIndex

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Indexing Global Strategy Assessments

When a user runs `validate_stationf_global`, the resulting five-axis analysis can be indexed by your agent. This means past strategic failures or successes become searchable knowledge, grounding future queries in verifiable data. Your RAG application doesn't just answer; it proves *why* an answer is correct based on structured global readiness scores.

Grounding Ambition Deficit Warnings

LlamaIndex can combine live API data with the output of the Prover. You build a unified index that combines operational metrics (e.g., current revenue) with strategic warnings (e.g., 'Ambition is provincial'). This allows your user to query not just facts, but systemic risk and potential market failure points.

Verifying Regulatory Moats

Use the Prover's analysis of regulations (like GDPR or CE marking) as a key document in your index. If a user asks about compliance, LlamaIndex retrieves the specific, structured reasoning that proves the regulation is a moat, not an obstacle. It provides highly precise answers grounded in established regulatory best practices.

Setup guide

Set up Station F Global 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 Station F Global 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 Station F Global Prover tools.",
)
response = await agent.run("List recent Station F Global 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 Station F Global 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 Station F Global Prover MCP in LlamaIndex

The tool generates structured reports that become ideal knowledge snippets. You can index the results of `validate_stationf_global` to build a highly reliable, queryable database of market viability.

Yes. You feed the Prover's output into your vector store and let users query against it. This grounds responses using real-world expansion criteria.

It handles business plans, financial models, market size projections, and complex compliance requirements like VAT OSS across 27 countries.

It enables you to combine internal documents (like old business plans) with external, rigorous analysis from the MCP Server for a complete picture of risk.

It provides highly structured data points that force developers to think about market access and systemic barriers rather than just local feature sets.

Use it with your favorite AI tools

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