Confident AI MCP Server

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LLM evaluation work tends to pile up in a separate browser tab from wherever the actual product conversation is happening — someone flags a bad model response, and checking whether it's a known regression means logging into Confident AI, finding the right run, and reading through traces by hand. Connect Confident AI to Neotask and that lookup becomes a question you can just ask: pull up evaluation results, annotations, and trace data for the systems you're testing straight from chat. The agent runs the query against the platform and hands back what it finds instead of you clicking through a dashboard.

• Pull evaluation results and traces for a specific run without opening the Confident AI dashboard
• Review annotations left on prior test cases when triaging a reported issue
• Check prompt management history alongside eval data in one conversation

What you can automate

Confident AI MCP Server

Run and review LLM evaluations, traces, and prompt management data on the Confident AI platform. Access annotations and evaluation results for AI systems you are testing or monitoring.

Real workflows

Triage a reported model regression

Someone reports an LLM system behaving oddly; ask Neotask to check Confident AI for recent evaluation runs and annotated failures on that system, and it returns the trace-level detail instead of you digging through the platform yourself.

Frequently asked questions

What can I actually check through this connection?

Evaluation results, trace data, annotations, and prompt management records on the Confident AI platform for any AI system you have access to there.

Does this let Neotask run new evaluations?

It gives the agent access to review existing evaluations, traces, and annotation data — it works from what is already on the Confident AI platform.