implementation

Integrate MCP-enabled Tools

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

Linked challenge: Recommender Multi-Agent for Complex Suggestions

Format

Text-first

Lines

Sections

Linked challenge

Recommender Multi-Agent for Complex Suggestions

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Develop a simulated external 'MusicDB API' and 'UserPreference API' that your agents can interact with. Implement MCP-enabled tools that allow your agents to call these APIs to fetch artist details, album metadata, and user listening history. Ensure that agent tasks are designed to appropriately call and interpret the results from these tools to enrich the recommendation process.

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

Variables

Lists

Code blocks

Purpose

implementation

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

Linked challenge

Recommender Multi-Agent for Complex Suggestions

This challenge tasks you with building a cutting-edge multi-agent recommendation system using CrewAI. Your system will leverage GPT-5 for highly creative and nuanced recommendation generation, supported by Claude Sonnet 4 for robust information retrieval and synthesis. Agents will collaborate to understand complex user intent, retrieve obscure related data via RAG, and generate highly personalized, multi-faceted recommendations for music or other domains. A critical component is the integration of MCP-enabled tools to access external data sources (e.g., music catalogs, user preference databases, external reviews) to enrich the recommendation process and provide context beyond the LLM's initial training data. The system must demonstrate advanced extended thinking capabilities, adapting its reasoning budget based on recommendation complexity.

Open challenge

Related prompts

Browse library

Design CrewAI Agent Roles and Tasks

planning

Implement RAG for Obscure Information

implementation

Orchestrate Extended Thinking and Adaptive Reasoning

implementation