How to Use the Toggl Plan MCP in LlamaIndex

Q: How do I find project details with Toggl Plan and LlamaIndex?

You get started by running listworkspaceprojects. Indexing these results lets your agent search for specific projects across all past sessions, acting like a knowledge base.

Q: Can I check milestone history with Toggl Plan and LlamaIndex?

Yes. By querying the output of listmilestones, you index markers that define major project stages. This helps your agent understand the historical flow of work.

Q: How does Toggl Plan handle task updates with LlamaIndex?

If a plan changes, you run updatetimelinetask. Indexing this action ensures that future queries about task status are accurate and grounded in the latest API data.

Q: Can I list team members using Toggl Plan and LlamaIndex?

Sure thing. Use listworkspacemembers to pull all user accounts into your index. This lets you build context around who was responsible for certain tasks.

Index scheduling history and project metadata using 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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Query historical task data

LlamaIndex lets you index past API results, turning raw output into searchable knowledge. You can run `get_task_details` and then query that specific record later—it's grounded in the actual data. This means your agent doesn't hallucinate; it retrieves answers based on previously indexed timeline task information.

Build project context indexes

Run `list_workspace_projects` and index those names. When you ask a question, the knowledge base pulls relevant projects automatically. This is great for RAG applications. Similarly, indexing results from `list_workspace_tags` lets your system answer questions like 'Which tag was used last quarter?'

Retrieve visual timeline context

You can use `get_timeline_view` to pull the scope of a project (since/until dates). Indexing this view allows your knowledge base to answer questions like 'What was the planned window for Q3?' This capability makes planning decisions based on historical API data.

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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Common questions about Toggl Plan MCP in LlamaIndex

You get started by running `list_workspace_projects`. Indexing these results lets your agent search for specific projects across all past sessions, acting like a knowledge base.

Yes. By querying the output of `list_milestones`, you index markers that define major project stages. This helps your agent understand the historical flow of work.

If a plan changes, you run `update_timeline_task`. Indexing this action ensures that future queries about task status are accurate and grounded in the latest API data.

Sure thing. Use `list_workspace_members` to pull all user accounts into your index. This lets you build context around who was responsible for certain tasks.

The server handles timeline task data, including names, start dates, end dates, and associated project information. All these structured scheduling details get indexed into the knowledge base.

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