How to Use the Bitso Exchange MCP in Pydantic AI

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Connect Bitso Exchange MCP to Pydantic AI

Create your Vinkius account to connect Bitso Exchange 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 Bitso Exchange with Pydantic AI

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Validate trades with Pydantic AI

Every response from `create_order` is checked against your Pydantic schemas. If the exchange returns a malformed price or weird volume, the agent crashes before it acts. This stops your agent from guessing what the API sent back. You get strict runtime validation for every single interaction.

Track open positions in Pydantic AI

Your agent fetches active orders using `list_open_orders` and maps them to your local data classes. It ensures your internal state matches the exchange state exactly. If the data doesn't match your expected types, the agent raises a validation error. You never deal with silent corruption or missing order fields.

Query ledger records in Pydantic AI

Need to audit your account? The agent pulls `list_ledger` data and forces it into your defined models. You get clean, typed objects to work with. This makes it trivial to calculate your P&L or check for missing deposits. The strict typing ensures your business logic remains solid.

Setup guide

Set up Bitso Exchange 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": {
        "bitso-exchange-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Bitso Exchange MCP in Pydantic AI

Define your Pydantic models for the tool inputs and outputs. The framework will automatically validate the MCP server response against these models at runtime.

The agent will catch the validation error if the response doesn't fit your schema. It prevents the agent from hallucinating data based on bad API responses.

Yes, because the framework is model-agnostic. You can switch between various LLMs while keeping your tool definitions and validation logic the same.

Use the MCPToolset class with your server URL. It handles the connection as a unified toolset you can drop directly into your agent definition.

The server runs in an ephemeral V8 sandbox. Since your code validates every object against a schema, unauthorized or unexpected data structures are rejected at the source.

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