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Route prompts intelligently, optimize AI costs, and track team usage in Cursor.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Prompt Go MCP" yet — see the docs or source repo.
Design a Prompt Go routing strategy for our Cursor engineering team: use high-quality models for code generation, and lower-cost models for everyday Q&A and refactoring advice. Explain routing rules and use cases for each request type.
A clear prompt routing plan with task categories, model assignment rules, and usage guidance.
Based on our team's AI usage in Cursor, create a real-time cost optimization plan that reduces expensive model calls without noticeably lowering code quality, and provide actionable recommendations.
A cost optimization recommendation covering savings opportunities, alternative model strategies, and implementation priorities.
Outline a team analytics framework for Prompt Go to review Cursor team prompt volume, cost distribution, common task types, and optimization opportunities, and suggest metrics suitable for a weekly report.
A team analytics metric set and weekly report structure to monitor efficiency and cost performance.
Manage Cursor agents with budget controls, monitoring, and inbox messaging.
Delegate coding, shell tasks, and codebase queries to Cursor AI.
Use Cursor MCP with a Google Docs agent for document automation and collaboration.
Get intelligent model recommendations for tasks and repositories in Cursor.
Compare AI models, estimate costs, and generate production-ready code in your IDE.
Invoke Cursor SDK agents from MCP clients for coding and ongoing conversations.