How to Use the Infinity Work Manager MCP in Pydantic AI

Q: How do I connect the Infinity Work Manager MCP server to Pydantic AI?

Use the unified MCPToolset class pointing to your Vinkius HTTP URL. Pass the toolset instance directly to the Agent constructor in the toolsets argument to expose all 15 tools like listitems and getboard.

Q: How does Pydantic AI handle schema mismatches with Infinity Work Manager data?

If getitem returns a payload that violates your defined Pydantic model, the framework raises a validation error. This prevents your agent from operating on malformed task attributes or corrupting your board states.

Build type-safe workspace automation using Pydantic AI to interact with your Infinity Work Manager boards.

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Connect Infinity Work Manager MCP to Pydantic AI

Create your Vinkius account to connect Infinity Work Manager 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 Work Manager with Pydantic AI

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Type-Safe Task Operations with Pydantic AI

The `get_item` tool fetches task data and validates the schema against Pydantic models at runtime to guarantee data integrity. If the Infinity API returns an unexpected payload or a missing attribute, Pydantic AI raises a validation error immediately. This prevents your agent from processing corrupt data. You can define strict schemas for custom attributes. The agent uses `list_attributes` to verify the board's setup before attempting to write new data, avoiding silent failures.

Validated Workspace Provisioning

The `create_folder` tool lets you build structured workspaces with guaranteed structural compliance. Pydantic AI ensures that folder hierarchies conform to your strict runtime models before hitting the API. This is paired with `create_board` to spin up complete, validated project environments. Your agent can confidently initialize workspaces for new teams, knowing that every folder and board meets your data constraints.

Controlled Task Updates via MCP Server

The `update_item` tool executes precise patches to task attributes while maintaining strict type boundaries. The agent translates natural language instructions into validated JSON payloads before making the MCP call. If the model attempts to pass an invalid string to a date field, the runtime intercepts the payload. This strict validation keeps your project boards clean and accurate.

Setup guide

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

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

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

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Common questions about Infinity Work Manager MCP in Pydantic AI

Use the unified `MCPToolset` class pointing to your Vinkius HTTP URL. Pass the toolset instance directly to the `Agent` constructor in the `toolsets` argument to expose all 15 tools like `list_items` and `get_board`.

If `get_item` returns a payload that violates your defined Pydantic model, the framework raises a validation error. This prevents your agent from operating on malformed task attributes or corrupting your board states.

Yes. You can configure your agent to parse user requests and call `create_item` with validated parameters. The framework guarantees that required fields like task names and folder IDs are present before sending the request.

The MCP integration supports both Streamable HTTP and SSE transports. Vinkius manages the server execution externally, so your Python application only needs to connect to the provided endpoint.

All task descriptions, comments, and user profiles are processed in memory within isolated V8 sandboxes. Vinkius handles the HTTPS transport encryption and never logs or stores the sensitive payloads sent to the Infinity API.

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