Rize MCP

Live

OAUTH 2.0

PRODUCTIVITY

Productivity

Connect to Rize MCP using OAuth 2.1 with MCP discovery and dynamic client registration. Access and analyze your time tracking data, projects, clients.

  • Acts as the user: Every tool call runs as the authorizing user. Access and audit trail stay intact.
  • Credentials stay vaulted: AES-256 encrypted, resolved at request time, never stored in LLM context.
  • Scoped before every call: Per-user permissions enforced automatically. 90-day audit trail included.
Rize MCP
agent · Acme Q3
Run
Add Note in Rize MCP
S
rizemcp_add_note
85ms
Rize MCP agent
Add a note about what you're working on. notes give rize context to improve time tracking accuracy. this is the primary.
Sources: Rize MCP
rizemcpmcp
1 tool call
18:29
Message Claude...

Rize MCP tools for AI agents

CALL ANY TOOL
12 tools covering approve, add.
rizemcp_list_tasks
List tasks
List tasks with their project and assignee associations. Use task IDs when creating or updating time entries.
Parameters
Name
Type
Required
Description
assigned_to_me
boolean
Optional
Only return tasks assigned to the current user
cursor
string
Optional
Pagination cursor
include_keywords
boolean
Optional
Include keywords (auto-tagging rules) for each task. Off by default for performance.
limit
number
Optional
Max tasks to return
project_ids
array
Optional
Filter tasks by project IDs
queries
array
Optional
Search tasks by multiple names. Cannot be combined with query; cursor is ignored and next_cursor is null.
query
string
Optional
Search tasks by name
statuses
array
Optional
Array of task statuses to include, for example ["in_progress", "completed"]. Do not pass a single string.
rizemcp_search_my_meetings
Search my meetings
rizemcp_list_teams
List teams
rizemcp_get_help
Get help
rizemcp_list_labels
List labels
rizemcp_get_skill
Get skill
rizemcp_list_skills
List skills
rizemcp_get_contract
Get contract
rizemcp_list_clients
List clients
rizemcp_get_login_url
Get login url
rizemcp_list_keywords
List keywords
rizemcp_get_report_run
Get report run
rizemcp_list_projects
List projects
rizemcp_get_time_entry
Get time entry
rizemcp_list_contracts
List contracts
rizemcp_get_routine_run
Get routine run
rizemcp_list_my_events
List my events
rizemcp_get_current_user
Get current user
rizemcp_list_report_runs
List report runs
rizemcp_get_product_docs
Get product docs
rizemcp_list_routine_runs
List routine runs
rizemcp_get_tagging_settings
Get tagging settings
rizemcp_list_team_members
List team members
rizemcp_get_org_profitability
Get org profitability
rizemcp_list_workspace_members
List workspace members
rizemcp_get_profitability_trend
Get profitability trend
rizemcp_list_my_apps_used
List my apps used
rizemcp_get_contract_profitability
Get contract profitability
rizemcp_list_my_time_entries
List my time entries
rizemcp_get_my_time_allocation
Get my time allocation
Build your Agent
Same auth pattern across every framework.
Python · LlamaIndex
from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="rizemcp")

mcp = MultiServerMCPClient({
"rizemcp": {
"url": "https://mcp.scalekit.com/rizemcp",
"headers": {"Authorization": "Bearer " + token}
}
})
tools = await mcp.get_tools()
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "rizemcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/rizemcp
// Pass: Authorization: Bearer + token
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "rizemcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/rizemcp
// Pass: Authorization: Bearer + token
from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="rizemcp")
# Connect to MCP at https://mcp.scalekit.com/rizemcp
# Pass: Authorization: Bearer + token
Try these prompts
Paste any prompt into your agent to get started.
Get started
Copy the prompt
Copied
Update an existing time entry?
Copy the prompt
Copied
Create a new Rize account via magic link?
Advanced
Copy the prompt
Copied
Reject pending AI-generated time entry suggestions?
Copy the prompt
Copied
Regenerate AI content for a pending or failed time entry?
SEE HOW AUTH WORKS
User authorises once. Every agent call after uses their token with scope enforcement.
1
Authorize
Your user connects
Rize MCP
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
Rize MCP
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
Rize MCP
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
Rize MCP
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 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
DevOps assistant agent
Polls GitHub for failing checks and stale PRs, opens Linear issues for the ones that need work, and posts a daily digest to Slack. It acts as the engineer, not a shared service account.
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.
People Ops and HR teams
Performance review collector
Collects review feedback from Airtable and Google Forms scoped to each manager's direct reports, writes per-employee summaries to Notion, and DMs the manager a Slack digest.
Test other agents
See the same per-user auth pattern across other 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
DevOps assistant agent
Poll GitHub for failing checks and stale pull requests, open Linear issues for the ones that need work, and digest 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.
PEOPLE OPS
Performance review collector agent
Collect review feedback from Airtable and Google Forms per manager, summarise each report in Notion, and DM the digest in Slack.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
01.
Shared tokens break per-user analytics
A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
One connector today. Ten 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 Rize as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Rize OAuth token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Rize?
Yes. Pass a tool name filter to listScopedTools so the productivity agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Rize.

What happens when a user revokes Rize 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.

Whose time tracking data can the agent read or approve?
Only the authorizing user's Rize workspace. Time entry approvals, client and contract edits, and notes run as that user, keeping personal productivity data personal.

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"": {
""rizemcp"": {
""url"": ""https://mcp.scalekit.com/rizemcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.rizemcp]
url = ""https://mcp.scalekit.com/rizemcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""rizemcp"": {
""url"": ""https://mcp.scalekit.com/rizemcp"",
""type"": ""http""
}
}
}