By default, every AI assistant answers tax questions the same wrong way: from memory. It recalls an averaged impression of tax law from whenever it was trained, which means last year's thresholds and the occasional deduction that doesn't exist where you live.

The fix is the same across every tool — give the assistant a verified source to read instead of letting it guess. How you connect that source differs slightly by agent. Here's how to do it for the three most common ones.

The mechanism underneath is the same for all of them: the OpenAccountants MCP server exposes verified tax rules — rates, thresholds, deductions, deadlines, computation steps — that the agent reads and applies instead of improvising. Below is the per-agent setup. The full, always-current install guide is here.

Claude

Claude supports MCP natively, which makes this the smoothest setup.

Open your MCP configuration (Claude Desktop's config file, or the connectors settings in the Claude apps).
Add the OpenAccountants MCP server endpoint.
Restart, and confirm the OpenAccountants tools appear as available.
Ask a tax question naming a country — Claude will fetch the relevant skill and apply it.

If you'd rather not configure MCP, you can also drop the open-source skill files straight into a Claude Project as knowledge — the agent will read from them directly.

ChatGPT

For ChatGPT, you have two solid paths:

Connector / MCP route: if you're on a plan and client that supports MCP connectors, add the OpenAccountants server the same way you'd add any connector, then ask your tax question.
Custom GPT / file route: create a Custom GPT and attach the relevant open-source skill files as knowledge, with an instruction to follow them rather than its own training data.

Either way, the goal is identical: ChatGPT reads the verified rule instead of recalling tax from memory.

Cursor (and other coding agents)

For developers building inside Cursor (or similar agentic IDEs):

Add the OpenAccountants MCP server to your MCP settings.
Your coding agent can now pull verified tax rules while it writes or reviews finance logic — so the tax computation in your code is based on real rules, not a plausible-looking guess.

This is especially valuable when you're shipping tax logic into production: the agent references the actual rates and thresholds as it writes, rather than hard-coding a number it half-remembered.

How to confirm it's actually working

Connecting the source is step one. Confirm the agent is using it:

Ask a question naming a specific country, then ask the agent to cite which rule it used. It should reference the skill, not vague "general knowledge."
Ask it what tax year the figures are for. A connected agent answers from the current rules; a guessing one waffles.
For anything you'll act on, run the five-point safety check before you rely on the number.

When the stakes are real

A connected agent is dramatically more reliable than a guessing one — but "more reliable" isn't "signed off." For a return that actually matters, have a qualified accountant review the output before you file. Verified rules plus a human signature is the combination you want.

Pick your agent, connect the source, confirm it's reading it. Start with the install guide — it takes a few minutes and it's the single highest-leverage change you can make to how your AI handles tax.