How to Use the Toggl Plan MCP in LlamaIndex

Q: Can I query past task changes with Toggl Plan and LlamaIndex?

Yes. By indexing tool outputs like updatetimelinetask, you can query past states of tasks and projects, getting answers based on recorded API data.

Q: How does the MCP Server help with user tracking in Toggl Plan using LlamaIndex?

You can run listworkspaceusers to pull a list of users. Indexing this output helps you answer questions like 'Who had access on Project X last month?'.

Q: Is the deletion process tracked by Toggl Plan and LlamaIndex?

When deletetimelinetask runs, that action's parameters can be logged and indexed, allowing you to track what was removed and when.

Build RAG applications by indexing Toggl Plan data with LlamaIndex.

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Connect Toggl Plan MCP to LlamaIndex

Create your Vinkius account to connect Toggl Plan to LlamaIndex 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 LlamaIndex

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Indexing Task Creation via MCP Server

The tool `create_timeline_task` allows you to write new tasks, and that output can become part of your searchable knowledge base. You just need the workspace ID, task name, and project ID. This means you query past sessions—like when a task was created—and get answers grounded in the actual API data.

Listing Projects with LlamaIndex

Need to know what projects exist? Use `list_workspace_projects` to pull all project names and IDs, which are then indexed. This lets you query configurations across many workspaces. If you need details on a single project, the `get_project_details` tool provides structured data for your index.

Updating Project Data with MCP Server

The `update_timeline_task` function lets you modify existing tasks. You pass it a JSON object describing the changes, and LlamaIndex captures that state change in your vector store. This is perfect for building RAG apps where you track how project phases shift over time.

Setup guide

Set up Toggl Plan MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Toggl Plan MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Toggl Plan tools.",
)
response = await agent.run("List recent Toggl Plan data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Toggl Plan. 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.

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Common questions about Toggl Plan MCP in LlamaIndex

The server provides structured data on projects, tasks, milestones, and user access. This allows the index to ground answers in real-time organizational API data.

Yes. By indexing tool outputs like `update_timeline_task`, you can query past states of tasks and projects, getting answers based on recorded API data.

You can run `list_workspace_users` to pull a list of users. Indexing this output helps you answer questions like 'Who had access on Project X last month?'.

This server touches project metadata, task descriptions, user IDs, workspace configurations, and milestone names. Everything is structured for indexing.

When `delete_timeline_task` runs, that action's parameters can be logged and indexed, allowing you to track what was removed and when.

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