implementation

Integrate MCP Tools for Human-in-the-Loop Feedback

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP

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Linked challenge

Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP

Prompt source

Original prompt text with formatting preserved for inspection.

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Detail the MCP-enabled tool integration for your CrewAI system. Explain how agents will interact with a simulated external feedback logging service (e.g., `log_issue(severity, description, content_id)`) and potentially trigger a human review process via an `escalate_for_human_review(content_id)` tool. Focus on the API specification for these tools and how agents will decide when to use them.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

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Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP

Following the backlash against AI-generated dubs, this challenge tasks you with building a sophisticated multi-agent system to ensure the quality and cultural appropriateness of AI-generated content (e.g., text, audio transcriptions). You will orchestrate a CrewAI team, utilizing GPT-5 for advanced critical analysis and Cohere Command R+ for robust RAG over style guides and cultural sensitivity documents. The system must detect subtle errors, cultural nuances, and potential 'hallucinations' that could lead to poor user experiences. This project emphasizes agent-to-agent (A2A) communication, adaptive thinking budgets, and MCP tool integration for a human-in-the-loop validation workflow. You will also employ DSPy to programmatically optimize agent prompts for higher evaluation accuracy, ensuring that AI-generated content meets stringent quality standards before deployment. The goal is to create a resilient and ethically aware content QA pipeline.

Open challenge

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