How to Use the Hullo MCP in Pydantic AI

Q: Can Hullo be used with Pydantic AI for member lookups?

Yes. Use getmember to fetch data and let your Pydantic model verify the structure. It’s the safest way to handle member info.

Enforce strict type safety in your Pydantic AI agent by using Hullo for validated coworking management operations.

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Connect Hullo MCP to Pydantic AI

Create your Vinkius account to connect Hullo 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 Hullo with Pydantic AI

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Perplexity

Strictly typed member management

Using `create_member` with Pydantic AI ensures your input data matches your model exactly. If the JSON is malformed, the agent rejects it before the call happens. `get_member` returns a typed object that your agent can trust. No more guessing about field types or missing values during runtime.

Reliable community engagement tools

Your agent uses `send_message` to communicate with members. Because Pydantic AI validates the response, you know the message was sent correctly. `list_conversations` returns clean data structures. You can iterate through threads without worrying about unexpected nulls or schema changes.

Type-safe MCP server interaction

Pydantic AI treats every tool as a validated function. This MCP server provides the necessary schemas to keep your agent's operation loop airtight. This approach eliminates silent corruption. If the server returns something weird, the agent hits a validation error immediately.

Setup guide

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

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

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

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Common questions about Hullo MCP in Pydantic AI

It validates every tool response against your defined models. If the data doesn't match, the agent throws a validation error.

Yes. Use `get_member` to fetch data and let your Pydantic model verify the structure. It’s the safest way to handle member info.

The agent halts execution. This prevents bad data from propagating through your system, keeping your state clean.

Not at all. Install the slim package, define your toolset, and connect to the HTTP endpoint. It works out of the box.

It exposes member profiles and message history strings. The server acts as a pass-through, and your Pydantic schemas enforce the security of that data.

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