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Develop MCP Tool Interfaces

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Linked challenge: Agent for Autonomous Market Intelligence

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Linked challenge

Agent for Autonomous Market Intelligence

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Implement MCP-enabled tool interfaces for fetching real-time stock quotes, searching financial news archives, and querying a simulated prediction market API (e.g., a simple Python mock API). Describe how your agents will 'discover' and 'invoke' these tools via the MCP.

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

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

Linked challenge

Agent for Autonomous Market Intelligence

This challenge tasks developers with building a sophisticated agentic system for real-time financial market analysis. The system will leverage the advanced reasoning capabilities of Gemini 2.5 Pro, particularly its 'Deep Think' mode, to process diverse financial data streams. Using LangGraph, you will orchestrate a dynamic, graph-based workflow where agents autonomously analyze news, company reports, and prediction market data (like from Kalshi or Polymarket, simulated) to generate actionable investment insights and risk assessments. The core of this challenge lies in implementing a hybrid reasoning system: instant processing for rapid market reactions and deep, extended thinking for complex economic forecasting. Agents will utilize the Model Context Protocol (MCP) for seamless tool integration, allowing them to fetch real-time stock prices, analyze financial statements via RAG, and query prediction markets. The goal is to create a robust, self-improving financial analyst capable of identifying opportunities and threats with high precision.

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