Taito.ai MCP is now generally available

Taito.ai’s Model Context Protocol (MCP) server is generally available today. Connect it to Claude, ChatGPT, Claude Code, OpenCode, or any other client that speaks the protocol, and you can read the people record and act on it in plain language, inside the tool you already have open. Every connection carries your own permissions, and that scope is what determines what it can see and do once it’s connected.

Connect the client you already use

Connect the client you already use once, from the integrations settings in Taito.ai. From there, the client itself lists what’s available: Claude, ChatGPT, Claude Code, OpenCode, Cursor, or any other tool that speaks the Model Context Protocol sees the same set of tools once it’s connected. You don’t pick a client because Taito.ai favors it; you connect whichever one your team already has open, and it works the same way in each.

Once connected, you prompt from there the way you’d ask a colleague: who’s starting next week, what’s a specific person’s current time-off balance, draft the welcome message for the new hire in engineering. The client turns that into the right tool call and brings back the answer, or does the thing, without you opening Taito.ai at all. For a longer walkthrough of what this looks like day to day, see how to use Claude to manage your HR database.

Your connection carries your own permissions

Connecting Taito.ai to a client doesn’t open up the organization’s people data to whoever’s holding the client. It opens up exactly what the connecting person can already see, which is a different question from whether an HR system supports MCP at all.

An admin who connects their own client can manage the organization the way they can inside the app: read and edit records, run reports across the company, and act on behalf of the team they administer. An employee who connects theirs reaches their own record, their own time off, their own documents, and nothing belonging to anyone else. The same scope applies whether a person is prompting directly or a workflow is running on their behalf automatically, down to individual fields; an automation built to touch only compensation data doesn’t get a side door into performance reviews just because it’s running as an agent instead of a person.

The same rule that governs every custom agent in Taito.ai governs this connection too: reminders, reporting, and data lookups can run on their own, but pay, terminations, contract terms, and anything else irreversible stop and wait for a person, whether the request came from a client, an agent, or a workflow built on top of either.

What people build on it

Two patterns show up most in what people build. The first is joining Taito.ai data with something else to answer a question neither system can answer alone: pulling headcount and attrition alongside a revenue number from a spreadsheet or a warehouse, then asking the client to build the report instead of exporting both sides and doing it by hand. The second is reaching Taito.ai from inside automation that already exists. A workflow built in n8n, for example, can call Taito.ai the same way it calls any other connected tool, so a process that already runs on a trigger, a new deal closing, a support ticket reaching a certain age, can read or update a people record as one step in a longer chain instead of a separate manual one.

Neither pattern requires Taito.ai to build the integration first. The protocol is the integration; what gets built on top of it is up to whoever’s building. See the catalog of worked examples for the shapes these take in practice.

Why an interface beats another point integration

The alternative to a protocol is a point integration: Taito.ai builds a connector to system A, then another to system B, and if A and B ever need to talk to each other, someone builds a third connector just for that. Every new system multiplies the number of connections that need building and maintaining.

An interface changes the shape of that problem. The protocol carries the data and the actions; the model layer does the joining, for whatever combination a person needs that day. Two systems don’t need to integrate with each other for a workflow to span both of them, because the client sitting on top can already reach each one directly. And because it’s the tool you already have open, using it doesn’t mean learning a new interface or a page you’ll forget exists in a month.

Taito.ai isn’t the only people operations system with an MCP server. For how the rest of the market compares, see which HR systems actually support MCP.

The Taito.ai MCP server is generally available on every account today. Connect it from the integrations settings; each user connects their own, and it carries their own permissions.