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Implementation Phase: Model Context Protocol Tool Integration for Data Retrieval

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Linked challenge: Optimize AI Data Center ROI

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Linked challenge
Optimize AI Data Center ROI

Prompt source

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Develop Python modules that mock enterprise data sources for energy costs (`get_energy_costs(location, date_range)`) and regulatory changes (`get_regulatory_status(location)`). Integrate these mock tools into your LangGraph agents using the Model Context Protocol (Model Context Protocol). Provide code snippets demonstrating how an agent (e.g., 'Financial Analyst' or 'Regulatory Compliance Agent') calls these Model Context Protocol-enabled tools and processes the structured data responses. Ensure error handling for tool failures.

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.