An MCP server lets an AI assistant — Claude, Cursor, or a custom agent — connect directly to your time-tracking data and act on it. Ask “which projects are over budget this month?” or say “log 45 minutes on the Acme onboarding call” in plain language, and the agent reads your real hours, projects, and budgets (or writes a draft time entry) with no exports, no glue code, and no browser. Timix.AI exposes exactly this at https://api.timix.ai/api/integration/v1/mcp, governed per API key so sensitive cost and contact data stay redacted by default.
That’s the short version. The longer version matters because how an AI reaches your operational data decides whether it’s genuinely useful — and whether it’s safe. Below is what MCP actually is, why time tracking is a natural fit, what an agent can and can’t do, and the data-governance model that lets you connect an AI without handing it your margins.
What is MCP (the Model Context Protocol)?
The Model Context Protocol is an open standard for connecting AI assistants to external tools and data. Introduced by Anthropic and now adopted across the AI-tooling ecosystem, it solves a simple problem: without a standard, every application has to build a bespoke plugin for every AI, and every AI has to learn every app. MCP flips that. A tool exposes one MCP server, and any MCP-capable client can use it.
The usual analogy is “USB-C for AI.” An MCP client (Claude Desktop, Cursor, or your own agent) connects to an MCP server, asks it what tools it offers, and then calls those tools on your behalf as the conversation requires. The AI does the wiring itself — you don’t.
Why time tracking belongs on MCP
The data inside a time-tracking system — hours logged, project status, budget burn, utilization, unbilled time — is exactly the data you’d want to ask an assistant about. It’s also the data that’s most painful to get at the moment you need it.
- Without MCP: you export a CSV, paste it into the chat, and hope it’s current — or you write and maintain API glue code. Both are stale the moment the next entry is logged, and neither lets the AI do anything.
- With MCP: the agent queries live data at the instant you ask, and it can act on it. “Is the Northwind project on track?” pulls the real numbers; “log the two hours I just spent on it” writes a real (draft) entry. No copy-paste, no export, no context-switch.
For a services firm, that turns your time-tracking system from a place you file data into a place an AI can reason over — during a standup, a client call, or a month-end review.
What an AI agent can do over Timix.AI’s MCP server
Once connected, an MCP-capable assistant can work with your account in plain language. On the read side it can pull things like:
- Hours & timesheets — hours summaries, a person’s logged time, your own recent entries.
- Projects & customers — project status, team, customer lists, and details.
- Budgets & billing — budget health and alerts, unbilled time, billing reports.
- Utilization — resource-utilization across the team.
On the write side it can take actions such as logging time or creating a customer or project. Crucially, those writes are created as drafts and are scoped to the role of the API key — an agent can never do something the key’s own user couldn’t do in the app, and nothing it creates is final until a human reviews it.
The part most tools get wrong: data governance
Connecting an AI to your business data is powerful — and, done carelessly, a liability. The moment an assistant can read your account, it can potentially read your cost rates, profit margins, and client contact details. That’s the risk that stops most firms from wiring an AI into their operations at all.
Timix.AI governs MCP access per API key, and it fails closed:
- The “Allow sensitive financial & contact data over MCP” option on each key is OFF by default.
- With it off, a connected AI sees revenue, hours, and budgets — but cost rates, profit margins, and contact details (email, phone, VAT) are redacted automatically before the data ever leaves the server.
- Every MCP call is audited, so you have a record of what was accessed.
- You turn the sensitive-data option on only for a key whose AI you explicitly trust with it.
The result: you can give an AI agent enough context to be useful about delivery and billing without exposing what each project actually costs you — a distinction most “connect your AI” integrations don’t make.
Connecting your AI client
Setup is deliberately short:
- Create a Timix.AI API key (the
bbl_…key on the API Keys page) — the MCP server is a Professional-plan feature. - Decide, per key, whether that AI may see sensitive financial and contact data (default: no).
- Point your MCP client at the endpoint:
https://api.timix.ai/api/integration/v1/mcpAny MCP-capable assistant — Claude, Cursor, or a custom agent — can then answer questions grounded in your live data. The step-by-step is in the tutorial The MCP server & AI assistant.
MCP vs. a plain REST API
Plenty of time trackers ship a REST API, and Timix.AI has one too (see Working with the API). The difference is who does the integration work:
- A REST API is for developers — you write code to fetch and format data for the AI, and you maintain it.
- An MCP server is for the AI itself — the assistant reads the list of available tools and calls them directly, so a non-developer can just ask a question in Claude or Cursor and get an answer grounded in real data.
An MCP server doesn’t replace the REST API; it sits alongside it for the growing set of workflows that run through an AI assistant instead of custom code. If your team is starting to work through Claude, Cursor, or an in-house agent, a time tracker that speaks MCP — with governance you control per key — meets them where they already are.