How to Use the Toggl Plan MCP in Pydantic AI

Q: How does the Pydantic AI framework handle Toggl Plan task updates?

When you call updatetimelinetask, Pydantic validates the entire input JSON object against a strict schema. If any field is wrong, the agent throws an error immediately, so you never get unexpected data.

Q: What kind of project information can I read with the Pydantic AI?

You use listworkspaceprojects to list them, and then getprojectdetails pulls specific metadata. The system guarantees that all returned data types are correct, whether it's an ID or a name.

Q: Is the Pydantic AI safe for creating tasks on Toggl Plan?

Yes. When using createtimelinetask, the framework validates that you provide all required parameters—workspace ID, task name, and project ID—before the call even hits the server.

Q: Can I use Pydantic AI to check users or tags in Toggl Plan?

Absolutely. You can pull user lists with listworkspaceusers and retrieve all available categorization tags using listworkspacetags. The response for both tools is fully validated.

Ensure perfect data correctness for Toggl Plan with Pydantic AI's type validation.

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Setup Toggl Plan with Pydantic AI

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Type-Safe Task Management

The `create_timeline_task` tool lets your agent create new tasks, and because of runtime validation, the agent will fail loudly if you don't provide a valid workspace ID or project ID. This means no silent data corruption. You can also update existing items using `update_timeline_task`. The Pydantic framework guarantees that the JSON structure matches exactly what Toggl Plan expects.

Validated Project Discovery

Getting a list of available projects is safe with `list_workspace_projects`, and fetching details using `get_project_details` always returns data matching predefined schemas. This prevents the agent from guessing at structure. Need to know who's involved? Use `list_workspace_users` or check all applicable tags by calling `list_workspace_tags`. The type safety keeps everything locked down.

Reliable Task State Reading

The agent reads task lists using `list_timeline_tasks`, and the results are guaranteed to adhere to a predictable Python object structure. You can trust that every returned item has the expected fields. Furthermore, when you only need specific info on one project, calling `get_task_details` or listing milestones with `list_milestones` provides highly validated, granular data.

Setup guide

Set up Toggl Plan 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": {
        "toggl-plan-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Toggl Plan MCP in Pydantic AI

When you call `update_timeline_task`, Pydantic validates the entire input JSON object against a strict schema. If any field is wrong, the agent throws an error immediately, so you never get unexpected data.

You use `list_workspace_projects` to list them, and then `get_project_details` pulls specific metadata. The system guarantees that all returned data types are correct, whether it's an ID or a name.

Yes. When using `create_timeline_task`, the framework validates that you provide all required parameters—workspace ID, task name, and project ID—before the call even hits the server.

Absolutely. You can pull user lists with `list_workspace_users` and retrieve all available categorization tags using `list_workspace_tags`. The response for both tools is fully validated.

The core data includes project IDs, workspace IDs, task names, user lists, and tag definitions. Pydantic ensures that these structured identifiers are always handled correctly.

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