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The NeetoPlaydash MCP server lets your AI assistant answer questions about your test results from plain-language requests. Ask it whether last night’s run passed, which tests failed, or whether a failure is new or a long-standing flake, and it works with your NeetoPlaydash workspace for you. MCP (Model Context Protocol) is an open standard that connects AI assistants to tools such as NeetoPlaydash. You describe the task; the assistant uses the right tool.

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What you can do

Find runs

List projects, and list a project’s runs filtered by branch or status.

Inspect failures

Read a run’s specs and tests, open one test’s attempts and Playwright error output, and group failing tests by the error they share.

Tell flakes from breaks

Read a test’s history across earlier runs, and a project’s daily trend, to see whether a failure is new.

Hand off the busywork

Ask your assistant to triage last night’s failures, find the shared cause, or tell you whether the suite is getting worse.

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MCP vs CLI: which should I use?

NeetoPlaydash’s CLI reaches the same resources MCP does - projects, runs, the specs and tests a run recorded, top errors, result histories, traces, and insights. MCP can also create a project. Otherwise, neither one can do more than the other, so choose on how the work reaches NeetoPlaydash.

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Reach for MCP when

  • The details live in your chat, not in your head. A stack trace someone pasted from Slack, a screenshot from a reviewer, or the diff you just wrote is weighed against the attempts and error output NeetoPlaydash recorded, with no retyping. The CLI cannot see any of it.
  • You have not decided the steps yet. Forty red tests can be one broken selector or forty separate regressions. Getting from the run to the cause means reading a failed attempt’s error output, judging what it points at, and only then choosing whether to open the trace, the earlier runs, or a different spec. A command can only carry out a decision you have already made.
  • One request should cover several steps. “Is this failure new, or has it been flaky for weeks?” walks the project’s runs, that run’s failing tests, one test’s attempts, and that test’s history across earlier runs, with no glue between commands.
  • The person doing it does not use a terminal. A QA lead who wants to know whether the suite is drifting simply asks. NeetoPlaydash hosts the server, so there is nothing to install or keep updated.

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Reach for the CLI instead when

  • No AI assistant should be in the loop. A cron entry or a CI step runs neetoplaydash runs get <run_id> --project <project_id> --quiet with nothing but the binary and a workspace it is already signed in to - no assistant open, no model account, no tokens spent per run. Every MCP call needs something with model access running.
  • The output feeds another program. The CLI prints the bare payload with --quiet, or JSON with --json, so a run’s status can drive a shell condition and a run’s top errors can go through jq into a Slack message or a spreadsheet. Here you get prose you would have to copy out by hand.
  • You are working through thousands of records. Here each page of a run’s tests is a separate tool call, and a whole suite crowds out the assistant’s context. The CLI returns total_records and total_pages next to the records, so a shell loop walks every page unattended and writes each one to a file or into jq - the size of the suite stops mattering.
  • The run has to be repeatable and reviewable. A command is the artifact: it records exactly what ran and repeats identically in a runbook or a pull request. Ask twice here and the assistant may take a different route.
You can have both. Run neetoplaydash setup claude and the same assistant drives the CLI for you, so a plain-language request still ends in an exact command you can read, repeat, and paste into a script.
Ask your assistant to show you what it plans to do before it creates a project. These tools act on your live workspace.

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What you need

  • An AI assistant that supports MCP, such as Claude, ChatGPT, Claude Code, Codex, Cursor, Gemini CLI, VS Code with GitHub Copilot, Windsurf, or Antigravity.
  • A NeetoPlaydash account. You need an API key only if the connection must belong to the workspace rather than to you, or if you use Antigravity. See Authentication.
Connect your assistant to get started.