How to Use the Albi Restoration MCP in Pydantic AI

Strictly typed Albi Restoration workflows for Pydantic AI, ensuring your agent never processes invalid project data.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Albi Restoration MCP to Pydantic AI

Create your Vinkius account to connect Albi Restoration 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.

GDPR Free for Subscribers

Setup Albi Restoration with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-Safe Restoration Records

Every response from the server is validated against your Pydantic models at runtime. When the agent calls `get_project_details`, it gets clean, structured data every time. If the API returns a malformed response, the agent stops immediately. This prevents bad data from corrupting your project records.

Validated Contact Creation

Adding a new contact is safe and predictable. The agent uses `create_new_contact` and the framework verifies the input types before sending the request. This ensures your database stays clean. You won't deal with the hidden errors that usually occur when passing raw JSON to an API.

Inventory and Role Auditing

Your agent audits project resources by running `list_inventory_equipment` and checking the output against your defined schemas. It also validates `list_defined_project_roles` to ensure the correct access levels. This setup gives you total confidence in the agent's actions. You know exactly what data is being read and how it maps to your internal logic.

Setup guide

Set up Albi Restoration 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": {
        "albi-restoration-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Albi. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Albi Restoration now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Albi Restoration MCP in Pydantic AI

It provides runtime validation for every tool output. If the API returns unexpected fields, the agent catches the error before acting on it.

Install the slim package and use the MCPToolset class to connect to the server. You pass it into your agent definition to start using the tools.

Yes, by defining your data schemas in Pydantic, the agent will reject any tool response that doesn't fit your requirements. It forces strict compliance.

Since every response is validated, you get clear error messages if the data fails your model. This makes tracking down API issues much faster.

Your project data is validated locally within your agent runtime before being processed. We use secure transports to ensure that your restoration records are never exposed during the validation phase.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript