How to Use the Bleez MCP in Pydantic AI

Q: Can I fetch suppliers for Pydantic AI via Bleez?

Simply call listsuppliers to get your data. The MCP server returns the payload, and your agent validates the structure before processing it.

Use Bleez with Pydantic AI for type-safe accounting automation in your Brazilian e-commerce stack.

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Connect Bleez MCP to Pydantic AI

Create your Vinkius account to connect Bleez 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 Bleez with Pydantic AI

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Strictly typed accounting via Pydantic AI

Your agent uses `create_accounting_entry` with guaranteed schema validation. If the input doesn't match the required format, the agent rejects it before hitting the server. Use `list_accounting_entries` to pull historical data into your typed models. This ensures you never have to deal with unexpected fields or malformed records.

Validate billing records in Pydantic AI

Call `create_invoice` and have the response validated immediately against your Pydantic models. This prevents silent failures when generating new billing documents. Fetch customer lists with `list_customers` to ensure your agent always uses valid IDs. The strict validation catches errors before they reach your accounting database.

Manage docs with Pydantic AI safety

Upload files using `upload_document` and verify the success response through your defined schemas. This makes document handling predictable and safe. Check `get_account_info` to ensure your environment is ready. Your Pydantic AI agent will flag any configuration issues immediately.

Setup guide

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

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

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

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Common questions about Bleez MCP in Pydantic AI

Pydantic AI forces every response to match your expected data structure. This stops the agent from hallucinating fields or breaking your accounting logic.

Yes, the server tools are designed to return data that fits perfectly into your Pydantic models. You get runtime validation for every single tool call.

Use your unique Vinkius endpoint token for the connection. The server remains in an isolated sandbox, and your agent logic enforces the security boundaries.

Simply call `list_suppliers` to get your data. The MCP server returns the payload, and your agent validates the structure before processing it.

Your journals and invoice details never leave the secure Vinkius environment during transit. We use transport-level encryption to keep your sensitive financial records private.

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