How to Use the Buenbit MCP in Pydantic AI

Use Pydantic AI with Buenbit to enforce strict type validation on every crypto trade and balance check.

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Connect Buenbit MCP to Pydantic AI

Create your Vinkius account to connect Buenbit 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.

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Setup Buenbit with Pydantic AI

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Perplexity

Type-safe trades with Pydantic AI

Every call to `create_order` is validated against your Pydantic models before the request leaves your machine. If the API schema changes, the agent catches the mismatch immediately. This prevents the agent from sending malformed trades. It forces the model to respect the exact data structure required by the exchange.

Validated balance checks

Retrieve portfolio data using `list_balances` and have it parsed into structured Python objects. Your agent can then perform logic on these balances with total confidence in the data types. Errors in the response are caught at the boundary. If the exchange returns an unexpected format, your agent fails before it can make a bad decision.

Operational control via MCP Server

Query `list_limits` to understand your current account boundaries. Your agent uses this data to adjust its strategy and avoid exceeding your trading thresholds. This server gives your agent a clear view of your account. You can trust the data flowing into your Python code because it is validated at runtime.

Setup guide

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

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

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

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Common questions about Buenbit MCP in Pydantic AI

You use the MCPToolset class from the pydantic-ai-slim package. Point it to your Vinkius server URL and add it to your agent's toolsets list.

The agent raises a validation error immediately. This stops the execution flow, preventing the model from acting on corrupted or hallucinated data.

Yes. You can swap your underlying model between OpenAI, Anthropic, or local LLMs without changing how your agent interacts with the Buenbit toolset.

The server supports both Streamable HTTP and SSE. You choose the transport that fits your environment, and the framework handles the rest.

The server runs in a zero-trust sandbox. It only exposes the tools you request, and your private API data never leaves the encrypted tunnel established during the tool call.

Use it with your favorite AI tools

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