How to Use the DecileHub MCP in Pydantic AI

Q: How is my private fund performance and LP data secured?

All venture capital details retrieved via getfundperformance or getinvestor flow through an isolated V8 sandbox runtime. Vinkius manages your API tokens securely, meaning your private fund performance data is never cached or leaked to the model.

Query venture fund metrics with absolute runtime type safety using Pydantic AI and DecileHub.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect DecileHub MCP to Pydantic AI

Create your Vinkius account to connect DecileHub 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.

GDPR Free for Subscribers

Setup DecileHub with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-safe investor tracking with Pydantic AI

Stop worrying about your agent hallucinating LP data or breaking on null fields. Pydantic AI validates every JSON response from `list_investors` and `get_investor` against strict Python schemas before your code ever runs over this MCP connection. If the API returns unexpected investor formats, the framework fails loudly with a validation error. This prevents corrupted LP records from sneaking into your downstream financial databases.

Strict validation for fund performance metrics

Track fund health with absolute mathematical certainty. The framework uses the DecileHub MCP Server to pull performance metrics via `get_fund_performance` and `list_funds`, validating numeric types instantly. Because Pydantic AI is model-agnostic, you can swap your LLM from Claude to GPT-4o without changing how your fund data is validated. Your schema checks remain rock-solid regardless of the model you use.

Validated portfolio valuations and filings

Pulling company valuations requires zero-error tolerance. Your agent calls `list_valuations` and `list_portfolio_companies` to audit your portfolio, ensuring every currency field matches your expected Pydantic models. When fetching filing reports with `get_filing_report`, the framework parses the metadata cleanly. You catch schema drift immediately if the underlying data structure changes, keeping your automated audits highly reliable.

Setup guide

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

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

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Decile Hub. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect DecileHub now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about DecileHub MCP in Pydantic AI

Use the unified MCPToolset class with your Vinkius HTTP endpoint. Pass this toolset directly to your Agent constructor to give it access to tools like list_companies_by_fund.

The framework immediately raises a validation error instead of passing bad data to the model. This guarantees that your agent never processes corrupt fund performance or valuation metrics.

Yes, Pydantic AI is model-agnostic. You can connect it to local models or commercial APIs while using the same MCP Server toolset to query get_company.

No, you should use the newer, unified MCPToolset approach. This ensures clean, long-term support for streaming tools like list_filings in your production Python apps using this MCP Server configuration.

All venture capital details retrieved via get_fund_performance or get_investor flow through an isolated V8 sandbox runtime. Vinkius manages your API tokens securely, meaning your private fund performance data is never cached or leaked to the model.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript