import { DynamicStructuredTool } from '@langchain/core/tools';
import { z } from 'zod';
import type { FastifyBaseLogger } from 'fastify';
import type { ResearchSubagent } from '../../harness/subagents/research/index.js';
import type { SubagentContext } from '../../harness/subagents/base-subagent.js';

export interface ResearchAgentToolConfig {
  researchSubagent: ResearchSubagent;
  context: SubagentContext;
  logger: FastifyBaseLogger;
}

/**
 * Creates a LangChain tool that delegates to the research subagent.
 * This is the standard LangChain pattern for exposing a subagent as a tool
 * to a parent agent.
 */
export function createResearchAgentTool(config: ResearchAgentToolConfig): DynamicStructuredTool {
  const { researchSubagent, context, logger } = config;

  return new DynamicStructuredTool({
    name: 'research',
    description: `Delegate to the research subagent for data analysis, charting, statistics, and Python script execution.

Use this tool for:
- Plotting charts with technical indicators (EMA, RSI, MACD, Bollinger Bands, etc.)
- Statistical analysis of price data
- Custom research scripts using the DataAPI and ChartingAPI
- Any task requiring code execution or matplotlib charts

The research subagent will write and execute Python scripts, capture output and charts, and return results.`,
    schema: z.object({
      name: z.string().describe('The name of the research script to create or update (e.g. "btc_ema_analysis"). Use the same name across calls to revise the same script rather than creating a new one.'),
      instruction: z.string().describe('The research task or analysis to perform. Be specific about what data, indicators, timeframes, and output you want.'),
    }),
    func: async ({ name, instruction }: { name: string; instruction: string }): Promise<string> => {
      logger.info({ name, instruction: instruction.substring(0, 100) }, 'Delegating to research subagent');

      const prompt = `Research script name: "${name}"\n\n${instruction}`;

      try {
        const result = await researchSubagent.executeWithImages(context, prompt);

        // Return in the format that AgentHarness.processToolResult() knows how to handle
        // (extracts images and passes them to channelAdapter)
        return JSON.stringify({
          text: result.text,
          images: result.images,
        });
      } catch (error) {
        logger.error({ error, errorMessage: (error as Error)?.message }, 'Research subagent failed');
        throw error;
      }
    },
  });
}