How to Use the Todoist MCP in LlamaIndex

Q: How does Todoist work with LlamaIndex for complex queries?

LlamaIndex takes the raw data from tools like listactivetasks and embeds it. This means you don't just get a list; you get contextually relevant answers grounded in your specific todo items.

Q: Can I use Todoist with LlamaIndex to find project details?

Yes. You can query the index using getprojectdetails output, allowing you to ask questions like, 'What is the stated goal of the Marketing Project?' and get an answer based on stored metadata.

Q: If I use Todoist with LlamaIndex, how does it handle task updates?

When updatetaskdetails runs, that new state can be indexed. This way, the knowledge base doesn't just show old data; it reflects the current status and history of the tasks.

Q: Does Todoist support fetching task details using LlamaIndex?

Absolutely. The gettaskdetails tool output is indexed, letting you retrieve specific information about any task—its description, due date, or associated project—by querying the knowledge base.

Turn Todoist data into knowledge for RAG applications. Built specifically for LlamaIndex.

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Connect Todoist MCP to LlamaIndex

Create your Vinkius account to connect Todoist 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 Todoist with LlamaIndex

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Create a searchable task index

LlamaIndex indexes the results of `list_projects` and `get_project_details`, turning your project hierarchy into a vector store. You can query past data—for instance, 'What was the goal of Project X?'—and get answers grounded in actual Todoist metadata. This indexed knowledge base allows you to combine live API calls with document retrieval. Your RAG application doesn't just list tasks; it reasons about them based on historical project context.

Track label usage across sessions

The `list_all_labels` tool output becomes a searchable index entry. Instead of just getting a flat list, you can query the knowledge base: 'Which projects frequently use the 'Urgent' label?' This provides semantic insight into your workflow. This approach is better than simple filtering because LlamaIndex combines the labels with project names and task details, giving context to every search result.

Build historical activity logs

You can index results from `list_comments` for both tasks and projects. This means your knowledge base retains a searchable history of conversations. You're not just seeing the comments; you're querying them based on topic or date. By indexing this data, LlamaIndex ensures that even if a task is closed and reopened using `reopen_task`, the historical conversation remains part of the accessible knowledge base.

Setup guide

Set up Todoist 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 Todoist 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 Todoist tools.",
)
response = await agent.run("List recent Todoist 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 Todoist. 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 Todoist MCP in LlamaIndex

LlamaIndex takes the raw data from tools like `list_active_tasks` and embeds it. This means you don't just get a list; you get contextually relevant answers grounded in your specific todo items.

Yes. You can query the index using `get_project_details` output, allowing you to ask questions like, 'What is the stated goal of the Marketing Project?' and get an answer based on stored metadata.

When `update_task_details` runs, that new state can be indexed. This way, the knowledge base doesn't just show old data; it reflects the current status and history of the tasks.

It exposes your personal label names and any associated task or project metadata. This allows you to index patterns—like finding all high-priority items across multiple projects based on a shared label.

Absolutely. The `get_task_details` tool output is indexed, letting you retrieve specific information about any task—its description, due date, or associated project—by querying the knowledge base.

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