How to Use the Flux Markets MCP in Pydantic AI

Q: How do I initialize the Flux Markets toolset in Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp" and use the unified MCPToolset constructor. Pass this toolset directly to your Agent instance in the toolsets parameter.

Q: Can I use the deprecated MCPServerHTTP class with Flux Markets in Pydantic AI?

No, you should use the unified MCPToolset class instead. The older HTTP class is deprecated in Pydantic AI and may cause connection issues with the hosted server.

Run type-safe energy market analysis with Pydantic AI and strict runtime data validation.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Flux Markets MCP to Pydantic AI

Create your Vinkius account to connect Flux Markets 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 Flux Markets with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Validate Flux Markets data at runtime with Pydantic AI

When dealing with energy prices, a single misplaced decimal point can ruin your model. Pydantic AI ensures that every payload returned by `get_live_tickers` matches your strict Python type definitions before your agent can act on it. If the Flux Markets server returns unexpected data formats, the Pydantic AI framework fails loudly with a validation error. This prevents your trading pipelines from processing corrupted or incomplete numbers, keeping your automated decisions accurate.

Parse swap settlements into clean Python models

Daily swap data can be messy and hard to parse reliably. By using `get_swap_settlements` within this MCP Server, your Pydantic AI agent automatically structures the raw API response into clean, typed schemas. You can write Pydantic AI agents that confidently extract Flux Markets pricing trends without worrying about silent failures. This Pydantic AI integration handles the translation from the raw endpoint output to your local Pydantic models.

Run energy pipelines across any LLM

This framework is completely model-agnostic, meaning you can swap your underlying LLM without changing your Flux Markets data fetching logic. Your Pydantic AI agent can query `get_cot_data` or `get_historical_tickers` using OpenAI, Anthropic, or even local models. Running cheap models for routine tasks like listing symbols via `list_symbols` keeps Pydantic AI pipeline costs down, leaving premium models for complex market analysis.

Setup guide

Set up Flux Markets 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": {
        "flux-markets-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Flux Markets 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 Flux Markets. 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 Flux Markets 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 Flux Markets MCP in Pydantic AI

Install the package using `pip install "pydantic-ai-slim[mcp]"` and use the unified `MCPToolset` constructor. Pass this toolset directly to your `Agent` instance in the toolsets parameter.

The framework will immediately raise a validation error, stopping the agent's execution. This prevents downstream analytics from running on malformed historical energy data.

No, you should use the unified `MCPToolset` class instead. The older HTTP class is deprecated in Pydantic AI and may cause connection issues with the hosted server.

Yes, the framework fully supports both Streamable HTTP and SSE transports. You can connect your agent to the Vinkius hosted endpoint using whichever transport fits your production architecture.

All authentication is handled on the Vinkius gateway, meaning your local Python environment never needs to store or expose raw API keys. The MCP agent only uses the secure token to read your energy data.

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