Toggl

@toggl show me all time entries I logged last week, grouped by project

Here's your time breakdown for last week (Dec 9-15): **Website Redesign** logged 18.5 hours across 12 entries; **Client Onboarding** logged 6 hours in 4 sessions; **Internal Meetings** logged 3.5 hours. Your longest single session was 4 hours on the redesign Thursday afternoon. Total tracked: 28 hours. Note: This is an illustrative summary—actual output depends on your Toggl workspace data and entry descriptions.

This example shows the MCP's read capability for time entries. Requires a valid API key with read access to your workspace. Toggl's API returns raw entry data; the AI synthesizes it into a readable summary. If you have multiple workspaces, specify which one to query.

@toggl start a new time entry for 'Q1 Planning' under the Strategy project, tag it as 'leadership'

Time entry started at 2:47 PM for project 'Strategy' with description 'Q1 Planning' and tag 'leadership'. Entry ID: 2847561923. The timer is now running in your Toggl workspace. To stop it, either use the Toggl app or ask me to stop the current entry. This is an illustrative confirmation—actual IDs and timestamps reflect your live workspace state.

This demonstrates the MCP's write capability to create time entries. The MCP can start timers but won't auto-stop them—you must manually stop or the entry runs indefinitely. Requires workspace ID and valid project/tag names; the MCP will error if those don't exist in your workspace.

@toggl review my time allocation this month and tell me if I'm spending too much on internal meetings versus client work

Analyzing your December entries: **Internal Meetings** consumed 34% of tracked time (41 hours), while **Client Work** (projects tagged 'billable') totaled 52% (63 hours). The remaining 14% went to admin tasks. Your meeting load is above the typical 25-30% benchmark for client-facing roles. Consider blocking focus time or declining non-critical meetings. This analysis is illustrative—actual percentages depend on your tagging and project setup.

This example pairs Toggl's data retrieval with the AI's analytical reasoning. The insight quality depends on how consistently you tag entries and categorize projects. The MCP fetches raw time data; the AI interprets patterns. No side-effects—this is read-only analysis.

When Toggl MCP beats manual time-entry for solo consultants

A freelance designer juggling four active clients needs to export time entries by client and project at month-end for invoicing. The Toggl MCP wins here because it exposes 31 tools that let you script the entire workflow: pull time entries by client, filter by date range, aggregate hours, and cross-reference project tags—all in one AI conversation instead of clicking through the Toggl web UI five times per client. The API key auth is straightforward for a solo operator. The trade-off: if you're only tracking time for yourself and billing one flat rate, the MCP is overkill—just use Toggl's native export. But if you're reconciling multiple clients with overlapping projects and need to slice time data in different ways each month, this MCP turns a 20-minute manual task into a 2-minute prompt.

Toggl MCP for onboarding new client projects at scale

A 12-person creative agency onboards three new clients per month, each requiring a Toggl workspace, client record, project structure, and tag taxonomy. The Toggl MCP's Create Organization, Create Client, Create Project, and Create Tag tools let you template the entire setup in a single AI workflow—no more copying last month's structure by hand or training junior PMs on Toggl's admin UI. The 31-tool surface area means you can also script cleanup: delete unused tags, archive old clients, and audit time-entry consistency across the team. The boundary: if your agency is under five people and onboards one client per quarter, the manual UI is faster. But at 10+ people with regular client churn, this MCP pays for itself in the first week by eliminating setup busywork and keeping your Toggl org tidy.

When Toggl MCP helps forecast support load from time data

A six-person customer support team tracks time in Toggl by ticket category (bug, feature request, onboarding) and wants to forecast next quarter's headcount needs based on actual time spent per category. The Toggl MCP's Get Client Details and time-entry retrieval tools let you pull historical data, group by tag, and feed it into a capacity model—all without exporting CSVs or writing custom API scripts. The 31 tools give you enough flexibility to slice time data by workspace, project, and tag in the same conversation where you're building the forecast. The catch: if your support team is under four people or you're only tracking total hours (not categorized time), the native Toggl reports are sufficient. But if you're planning headcount or rebalancing workload across categories, this MCP turns Toggl into a planning input instead of just a timesheet.