How to Use the Toggl Plan MCP in Pydantic AI

Q: How do I see the overall project timeline using Toggl Plan and Pydantic AI?

You call gettimelineview and supply a date range. This returns the full visual view of tasks, letting your agent check resource allocation across time.

Q: Is it safe to delete segments using Toggl Plan and Pydantic AI?

Be extremely cautious. deletetimelinetask permanently removes data from the timeline. It's an action that leaves no undo button.

Q: What happens if I try to update data with Toggl Plan and Pydantic AI?

The agent uses updatetimelinetask. Since you're using Pydantic, it validates the JSON object. If the data is bad, the agent fails loudly—you know exactly why.

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Connect Toggl Plan MCP to Pydantic AI

Create your Vinkius account to connect Toggl Plan 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 Toggl Plan with Pydantic AI

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Create and Update Segments

To start a new segment, use `create_timeline_task`—you'll need the name plus clear start and end dates. If you gotta tweak an existing task, call `update_timeline_task` with your data.

Check Task Status

Need to know what a specific segment is doing? Run `get_task_details`. For the big picture, `list_milestones` shows all major markers on the timeline.

Audit Workspace Context

Before acting, check the scope. Use `list_workspace_members`, or run `list_workspace_projects` and `list_workspace_tags` to get a full inventory of what's available in the system.

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-1-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

You call `get_timeline_view` and supply a date range. This returns the full visual view of tasks, letting your agent check resource allocation across time.

Be extremely cautious. `delete_timeline_task` permanently removes data from the timeline. It's an action that leaves no undo button.

You can get a list of all projects, tags, and team members using `list_workspace_projects`, `list_workspace_tags`, or `list_workspace_members`.

The agent uses `update_timeline_task`. Since you're using Pydantic, it validates the JSON object. If the data is bad, the agent fails loudly—you know exactly why.

This server handles project structure and team assignments: member lists, project names, milestones, and task segment definitions. It focuses on work allocation data.

Use it with your favorite AI tools

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