Toggl 2.0 MCP server for AI agents

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BEARER TOKEN

WORK MANAGEMENT

Productivity

Toggl 2.0 gives your agent work management with time tracking: plan and update tasks and milestones, start and stop timers, log time against tasks, and check timesheets, time off, and who is out.

  • Per-user credentials: each call uses the actual user's access token, never a shared bot.
  • Encrypted per-tenant vault: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: pre-call scope check, 90-day SIEM-exportable audit chain.
Toggl 2.0
agent · Acme Q3
Run
Who on the team is out this week?
S
togglfocus_list_timeoff_whos_out
99ms
Work agent
2 people have time off this week. Dana is out Monday to Wednesday and Leo is out on Friday.
Sources: Toggl 2.0 time off, 2 members
togglfocus
2 members
18:29
Message Claude...

Tools your work management agent reaches for on Toggl 2.0, scoped per user.

CALL ANY TOOL
67 tools for work management in Toggl 2.0: tasks, subtasks, statuses, milestones, projects, clients, and tags, live timers and time entries, timesheets, goals, and time off.
togglfocus_add_project_members
Add project members
Add users and teams to a project with a given role. Returns no content on success (HTTP 204). Use to grant access to a project. Use togglfocus_remove_project_member to revoke it. Needs organization_id and workspace_id. togglfocus_get_user_settings returns current_workspace_id (and mcp_active_organization_id when set). role_id comes from togglfocus_list_organization_roles.
Parameters
Name
Type
Required
Description
organization_id
integer
Required
Numeric ID of the Toggl 2.0 organization that owns the workspace. Example: 1234567
project_id
integer
Required
Numeric ID of the project. Find it with togglfocus_list_projects. Example: 1234567
role_id
integer
Required
Numeric ID of the project role granted to the added users and teams. Find it with togglfocus_list_organization_roles. Example: 1234567
workspace_id
integer
Required
Numeric ID of the Toggl 2.0 workspace to operate in. Example: 1234567
group_ids
array
Optional
Numeric IDs of teams (user groups) to add to the project. Find them with togglfocus_list_organization_teams. Accepts a JSON array. Example: [123456, 234567]
user_ids
array
Optional
Numeric IDs of users to add to the project. Find them with togglfocus_list_organization_users. Accepts a JSON array. Example: [123456, 234567]
togglfocus_archive_project
Archive project
togglfocus_create_client
Create client
togglfocus_create_project
Create project
togglfocus_create_tag
Create tag
togglfocus_create_task_time_entry
Create task time entry
togglfocus_delete_client
Delete client
togglfocus_delete_project
Delete project
togglfocus_delete_tag
Delete tag
togglfocus_get_client
Get client
togglfocus_get_milestone
Get milestone
togglfocus_get_status
Get status
togglfocus_get_task
Get task
togglfocus_get_time_entry
Get time entry
togglfocus_list_clients
List clients
togglfocus_list_milestones
List milestones
togglfocus_list_organization_teams
List organization teams
togglfocus_list_projects
List projects
togglfocus_list_subtasks
List subtasks
togglfocus_list_tasks
List tasks
togglfocus_list_timeoff_entries
List timeoff entries
togglfocus_log_planned_time_entry
Log planned time entry
togglfocus_remove_project_member
Remove project member
togglfocus_search
Search
togglfocus_start_tracking
Start tracking
togglfocus_stop_tracking
Stop tracking
togglfocus_unarchive_project
Unarchive project
togglfocus_update_client
Update client
togglfocus_update_project
Update project
togglfocus_update_tag
Update tag

For more tools, view docs.

Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
import { ScalekitClient } from "@scalekit-sdk/node";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

// Toggl 2.0 tools scoped to this user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["togglfocus"], toolNames: [
    "togglfocus_list_tasks",
    "togglfocus_start_tracking",
    "togglfocus_stop_tracking"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm, tools });
await agent.invoke({ messages: [{ role: "user", content: "Start tracking time on the Draft Q4 report task" }] });
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["togglfocus"] }, pageSize: 100,
});

const res = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Start tracking time on the Draft Q4 report task" }],
  tools,
});

// Execute the tool call with the user's vaulted Toggl 2.0 access token
await sk.tools.executeTool(res.choices[0].message.tool_calls[0], "user_123");
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["togglfocus"] }, pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-5",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Start tracking time on the Draft Q4 report task" }],
  tools,
});

// Tool call runs with the user's vaulted Toggl 2.0 access token
await sk.tools.executeTool(msg.content, "user_123");
import { Agent } from "@google/adk/agents";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["togglfocus"] }, pageSize: 100,
});

const agent = new Agent({
  name: "work_agent",
  model: "gemini-2.5-pro",
  instruction: "Toggl 2.0 tools scoped to this user",
  tools,
});

await agent.run("Start tracking time on the Draft Q4 report task");
Try these prompts
Copy any prompt into your agent. Each maps directly to a Toggl 2.0 tool. Click to copy, paste into your agent, done.
Tasks
Copy the prompt
Copied
Create a task called Draft Q4 report in the Planning project assigned to me.
Copy the prompt
Copied
List my open tasks due this week.
Copy the prompt
Copied
Create a milestone for the beta launch due November 30.
Time
Copy the prompt
Copied
Start tracking time on the Draft Q4 report task.
Copy the prompt
Copied
Stop my running timer.
Copy the prompt
Copied
Log 90 minutes on the Client onboarding task for yesterday afternoon.
Team
Copy the prompt
Copied
Who is out this week?
Copy the prompt
Copied
Show the time accounting report for October.
Copy the prompt
Copied
List the timesheets for last week.
SEE HOW AUTH WORKS
Each user connects their own Toggl 2.0 access token once, and Scalekit sends it with every call. Access tokens stay vaulted, every call is scope checked, and every action is logged.
1
Authorize
Your user connects
Toggl 2.0
once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access
Who:
user ‘A’
when:
Once per user
access:
Limited to user
2
Store
Their
Toggl 2.0
token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection
vault:
encrypted
scope:
per-user
tokens:
auto-refreshed
3
Resolve
When your agent calls a
Toggl 2.0
tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs
speed:
~40ms
check:
before every call
seen by:
nobody
4
Audit
Every
Toggl 2.0
tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it
history:
90 days
export:
SIEM-ready
logged:
every call
Test other agents
See the same per-user auth pattern across other time tracking and project management connectors.
Engineering Teams
Engineering standup agent
Pulls commits from GitHub and GitLab, tracks issue movement in Jira, and posts a per-engineer standup brief to Slack. Each engineer's activity is read on their own delegated OAuth.
Engineering Teams
Slack triage
Polls Slack for new messages, classifies bugs and support requests with a LangGraph router, files GitHub issues or Zendesk tickets, and confirms in the thread.
Test other agents
See the same per-user auth pattern across other time tracking and project management connectors.
ENGINEERING
Engineering standup agent
Pull commits from GitHub and GitLab, track Jira issue movement, and post a per-engineer standup brief to Slack.
ENGINEERING
Slack triage agent
Classify new Slack messages as bugs or support requests, file the GitHub issue or Zendesk ticket, and reply in the thread.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
01.
Shared tokens break per-user analytics
A shared Toggl 2.0 access token looks fine in a demo. In production every timer and task update looks like one service account, and you cannot tell which user triggered it. Scalekit resolves the credential of the actual user who triggered the agent, never a shared bot.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
Toggl 2.0 today. Ten connectors tomorrow.
“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”
Venu Madhav Kattagoni
Head of Engineering / Von
FAQs
Frequently Asked Questions
Does the agent access Toggl 2.0 as the user or through a shared token?
As the user. Each user connects their own Toggl 2.0 access token once, and Scalekit sends it with every call. Timers, tasks, and time entries the agent creates are attributed to that user in your audit trail, not a shared service account.
Where is the Toggl 2.0 access token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. To rotate it, the user reconnects with a new access token. Revocation is a single dashboard action. Credentials never appear in prompts, logs, or LLM context.
Can I limit what the agent does in Toggl 2.0?
Yes. Filter by tool name in listScopedTools to expose only what you want, for example the list and timer tools without togglfocus_delete_task or togglfocus_delete_project. The archive tools hide items without deleting them. Scalekit also enforces scope checks before every API call.
What happens when a user revokes Toggl 2.0 access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.
Can the agent start a timer from a plain task name?
Yes. togglfocus_start_tracking_from_description resolves a task from a name, project name, and tags, creating it if needed, then starts a timer or logs a completed entry. togglfocus_start_tracking starts a timer when the agent already has the task or project ID.
Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""togglfocus"": {
""url"": ""https://mcp.scalekit.com/togglfocus"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.togglfocus]
url = ""https://mcp.scalekit.com/togglfocus""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""togglfocus"": {
""url"": ""https://mcp.scalekit.com/togglfocus"",
""type"": ""http""
}
}
}