Where do I obtain my Toggl Plan Access Token?

Navigate to the Toggl Plan web panel, head over to your **Profile settings**, and locate the Developer/API Integrations segment. You will need a valid Access Token (often labeled API or OAuth Bearer token in your administrative view). Create it permanently and keep the exposed hash secure.

Does the system support permanent deletion of elements?

Yes. This connector features a `delete_timeline_task` handler. If specifically instructed by you, the AI agent is capable of making a permanent removal request which cannot be un-done. You should instruct the AI cautiously when using mutations.

Are there any data sync delays when updating timelines with the agent?

No. Toggl Plan's API ensures immediate reflection of all timeline and milestone changes. When the agent updates a task or resource allocation, it instantly appears in the web interface for all workspace users without any caching delays.

Toggl Plan MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Toggl Plan
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Toggl Plan through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "toggl-plan": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Toggl Plan, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Toggl Plan MCP Server

Connect your Toggl Plan workspaces to an AI agent entirely bypassing the complex graphical interfaces. Allow your project managers and team leads to directly read, create, and organize workload data, milestones, and daily tasks inside a conversational or command-driven environment.

LangChain's ecosystem of 500+ components combines seamlessly with Toggl Plan through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Timeline Oversight — Search workspaces to list, read, or inspect the metadata details of specific timeline tasks and milestones
Project Construction — Easily list all the active project segments directly on your terminal to know what your team is facing today
Task Execution — Complete the full cycle of task management: Create new nodes on the timeline, update existing entries, or delete deprecated ones through simple instructions
Fleet Operations — Manage human resources by securely listing all registered workspace users to assign workloads correctly
Taxonomy Organization — Check and retrieve current tagging structures to ensure standardized labels

The Toggl Plan MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Toggl Plan to LangChain via MCP

Follow these steps to integrate the Toggl Plan MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Toggl Plan via MCP

Why Use LangChain with the Toggl Plan MCP Server

LangChain provides unique advantages when paired with Toggl Plan through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Toggl Plan MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Toggl Plan queries for multi-turn workflows

Toggl Plan + LangChain Use Cases

Practical scenarios where LangChain combined with the Toggl Plan MCP Server delivers measurable value.

RAG with live data: combine Toggl Plan tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Toggl Plan, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Toggl Plan tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Toggl Plan tool call, measure latency, and optimize your agent's performance

Toggl Plan MCP Tools for LangChain (10)

These 10 tools become available when you connect Toggl Plan to LangChain via MCP:

create_timeline_task

Requires workspace ID, task name, and project ID. Creates a new task on the Toggl Plan timeline

delete_timeline_task

This action is irreversible. Permanently deletes a task from the timeline

get_project_details

Retrieves details for a specific project

get_task_details

Retrieves details for a specific timeline task

list_milestones

Lists all project milestones

list_timeline_tasks

Requires a workspace ID. Lists all tasks on the Toggl Plan timeline for a specific workspace

list_workspace_projects

Lists all projects in a specific Toggl Plan workspace

list_workspace_tags

Lists all tags used for task categorization

list_workspace_users

Lists all users with access to the workspace

update_timeline_task

Provide updates as a JSON object. Updates an existing timeline task

Example Prompts for Toggl Plan in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Toggl Plan immediately.

"List all active projects in Workspace 992211."

"Create a timeline task named 'Re-authenticate module' in Project 19332, workspace 992211."

Troubleshooting Toggl Plan MCP Server with LangChain

Common issues when connecting Toggl Plan to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Toggl Plan + LangChain FAQ

Common questions about integrating Toggl Plan MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.