How to Use the Melo MCP in Pydantic AI

Validate all on-chain real estate data with Melo and Pydantic AI for type-safe, error-free agent logic.

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Connect Melo MCP to Pydantic AI

Create your Vinkius account to connect Melo 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 Melo with Pydantic AI

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Perplexity

Type-safe property queries in Pydantic AI

Every response from `get_property` is validated against your Pydantic models. If the blockchain returns malformed data, your agent catches it immediately. This prevents silent failures in your logic. You can trust that the data moving through your system matches your defined schema exactly.

Strict validation of Melo tools

Use `list_transactions` and `get_market_insights` with full type enforcement. The Pydantic AI framework ensures that every field is accounted for before your agent proceeds. This is essential for production systems where data integrity is not optional. You won't have to deal with hallucinated fields or unexpected null values in your records.

Reliable on-chain history tracking

Get accurate, typed records of property history using `get_property_history`. The data is parsed into your models, making it easy to perform calculations on past sales. Your agent handles the history without worrying about schema drift. It's a clean, predictable way to manage complex real estate timelines.

Setup guide

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

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

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

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Common questions about Melo MCP in Pydantic AI

The framework validates every MCP tool response against your defined models. If the server output doesn't match, the agent throws an error before taking any action.

It provides a robust layer of safety. You get the speed of on-chain data with the guarantee that your agent logic won't break due to bad data.

Yes. This MCP server is model-agnostic. You can pair it with any LLM while using Pydantic AI to enforce your data constraints.

Your agent will trigger a validation error immediately. This stops the process and prevents bad data from propagating through your investment models.

The server communicates over a standard protocol. It only queries public blockchain ledger data, ensuring that no sensitive off-chain info is exposed during validation.

Use it with your favorite AI tools

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