How to Use the Infinity MCP in Pydantic AI

Q: How does Pydantic AI validate Infinity API responses?

The framework parses the JSON returned by tools like listboarditems directly into Pydantic schemas. If the API structure changes, Pydantic AI raises a validation error instead of passing bad data.

Q: How do I register the Infinity toolset in Pydantic AI?

Use the unified MCPToolset class with your Vinkius HTTP URL. This connects the MCP server directly to your agent when initializing the toolsets list.

Q: Can I use Pydantic AI to safely delete Infinity items?

Yes, you can call removeboarditem within your agent workflow. Pydantic AI validates the item ID structure first, ensuring your agent doesn't execute deletions with malformed identifiers.

Build type-safe Pydantic AI workflows that interact with Infinity boards with strict runtime schema validation.

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Connect Infinity MCP to Pydantic AI

Create your Vinkius account to connect Infinity 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 Infinity with Pydantic AI

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Validate Infinity board attributes with Pydantic AI

Stop letting your agent write garbage data to your project boards. Pydantic AI uses strict Python type definitions to validate every field returned by `list_board_attributes` before any action runs. The MCP server rejects malformed payloads before they reach the API, raising a validation error immediately so you catch bugs during development.

Secure MCP Server comment parsing using Pydantic AI

Extract project discussions cleanly using `list_item_comments` without worrying about unstructured text breaking your code. Pydantic AI forces the raw API responses into typed Python models at runtime. Your code defines the exact structure you expect for comments. If the server returns unexpected fields, the system fails loudly, preventing corrupted state files.

Type-safe Folder Navigation

Query your workspace structure using `list_board_folders` with complete confidence. Pydantic AI ensures the folder IDs and parent-child relationships match your codebase's type definitions. You can safely write logic to move items using `update_board_item`. The framework validates the target folder exists before the agent ever executes the update.

Setup guide

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

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

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

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Common questions about Infinity MCP in Pydantic AI

The framework parses the JSON returned by tools like `list_board_items` directly into Pydantic schemas. If the API structure changes, Pydantic AI raises a validation error instead of passing bad data.

Use the unified `MCPToolset` class with your Vinkius HTTP URL. This connects the MCP server directly to your agent when initializing the toolsets list.

Yes, you can call `remove_board_item` within your agent workflow. Pydantic AI validates the item ID structure first, ensuring your agent doesn't execute deletions with malformed identifiers.

The toolset is completely model-agnostic. You can run your type-safe agent workflows using Anthropic, Gemini, OpenAI, or local models running on your own machine.

Vinkius routes all requests through isolated V8 sandboxes. This secure MCP setup processes your board credentials and project schema definitions ephemerally, ensuring your access tokens are never logged or stored.

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