PromptLayer MCP

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If your team is running LLM prompts in production, PromptLayer is probably where the version history, request traces, and evaluation scores live — but checking it usually means opening a separate dashboard mid-conversation. Connected to Neotask, you can ask directly: which version of a prompt is live, how did the last evaluation run score, what did a specific request trace actually send and receive. The agent pulls it from PromptLayer and answers in the same thread you're already working in.

• Browses prompt versions and their change history
• Pulls request traces to see exactly what was sent and returned
• Surfaces evaluation results and dataset details for monitoring LLM app behavior

What you can automate

PromptLayer MCP

Browse prompt versions, request traces, evaluations, and datasets in PromptLayer to monitor and manage LLM application behavior.

Real workflows

Debug a regression in an LLM feature

Ask what changed in a prompt's version history around the time a feature started behaving oddly, then pull the request traces from that window to see the actual inputs and outputs PromptLayer recorded.

Check evaluation health before shipping

Ask for the latest evaluation results on a given prompt or dataset in PromptLayer, so you know its performance before you promote a new version.

Frequently asked questions

What does the PromptLayer connection let Neotask do?

Browse prompt versions, request traces, evaluations, and datasets in PromptLayer to help monitor and manage LLM application behavior.

Can I still use the PromptLayer dashboard directly?

Yes — this just means you don't have to leave chat to check version history, traces, or eval scores.