import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { Ollama, type Message, type ToolCall } from "ollama";
import { log } from "../utils/cli.ts";
import { retry } from "../utils/retry.ts";
import { agent, type AgentResult, type AgentRunContext } from "./shared.ts";

const DEFAULT_MODEL = "qwen3.6:35b";
const MAX_ITERATIONS = 100;

interface OllamaTool {
  type: "function";
  function: {
    name: string;
    description: string;
    parameters: Record<string, unknown>;
  };
}

async function buildMcpClient(mcpServerUrl: string): Promise<Client> {
  const client = new Client(
    { name: "shockbot-agent", version: "0.1.0" },
    { capabilities: {} },
  );
  const transport = new StreamableHTTPClientTransport(new URL(mcpServerUrl));
  await client.connect(transport);
  return client;
}

async function getOllamaTools(mcpClient: Client): Promise<OllamaTool[]> {
  const { tools } = await mcpClient.listTools();
  return tools.map((t) => ({
    type: "function" as const,
    function: {
      name: t.name,
      description: t.description ?? "",
      parameters: (t.inputSchema as Record<string, unknown>) ?? {
        type: "object",
        properties: {},
      },
    },
  }));
}

async function callMcpTool(
  mcpClient: Client,
  toolName: string,
  args: Record<string, unknown>,
): Promise<string> {
  try {
    const result = await mcpClient.callTool({
      name: toolName,
      arguments: args,
    });
    const content = result.content as
      | Array<{ type: string; text?: string }>
      | undefined;
    if (!content || content.length === 0)
      return JSON.stringify({ success: true });
    const text = content
      .map((c) => (c.type === "text" ? (c.text ?? "") : ""))
      .filter(Boolean)
      .join("\n");
    return text || JSON.stringify(result);
  } catch (err) {
    const msg = err instanceof Error ? err.message : String(err);
    log.debug(`Tool ${toolName} error: ${msg}`);
    return JSON.stringify({ error: msg });
  }
}

/**
 * When context approaches the limit, truncate the content of old tool-result
 * messages to free space. Keeps the most recent N tool results intact so the
 * model still has fresh context; replaces earlier ones with a size notice.
 * Never touches system/user/assistant messages — only tool messages.
 */
function pruneToolMessages(messages: Message[], keepRecent = 6): Message[] {
  const toolIndices: number[] = [];
  for (let i = 0; i < messages.length; i++) {
    if (messages[i].role === "tool") toolIndices.push(i);
  }
  const pruneCount = Math.max(0, toolIndices.length - keepRecent);
  if (pruneCount === 0) return messages;

  const toPrune = new Set(toolIndices.slice(0, pruneCount));
  let pruned = 0;
  const result = messages.map((msg, i) => {
    if (!toPrune.has(i)) return msg;
    const originalLen =
      typeof msg.content === "string" ? msg.content.length : 0;
    pruned++;
    return {
      ...msg,
      content: `[pruned: was ${originalLen} chars — context limit approached]`,
    };
  });
  log.info(`» pruned ${pruned} old tool message(s) to reduce context`);
  return result;
}

async function unloadModel(ollama: Ollama, model: string): Promise<void> {
  try {
    await ollama.generate({ model, keep_alive: 0, prompt: "" });
    log.info(`» unloaded model ${model} from Ollama`);
  } catch (err) {
    const msg = err instanceof Error ? err.message : String(err);
    log.warning(`» failed to unload model ${model}: ${msg}`);
  }
}

async function runOllamaLoop(ctx: AgentRunContext): Promise<AgentResult> {
  const ollamaHost = process.env.OLLAMA_HOST ?? "";
  if (!ollamaHost) {
    const errorMsg =
      "OLLAMA_HOST environment variable is not set. Please set it to the URL of your Ollama instance.";
    log.error(errorMsg);
    return { success: false, error: errorMsg };
  }

  const model = ctx.model ?? process.env.OLLAMA_MODEL ?? DEFAULT_MODEL;
  const numCtx = ctx.payload.contextWindow ?? 262144;

  log.info(`» connecting to Ollama at ${ollamaHost}, model ${model}`);

  const ollama = new Ollama({ host: ollamaHost });
  const mcpClient = await buildMcpClient(ctx.mcpServerUrl);

  log.info("» fetching MCP tool list...");
  const tools = await getOllamaTools(mcpClient);
  log.info(`» ${tools.length} tools available`);

  let messages: Message[] = [
    {
      role: "user",
      content: ctx.instructions.full,
    },
  ];

  // Tools that signal the agent has produced its final output.
  const OUTPUT_TOOLS = new Set([
    "create_pull_request_review",
    "report_progress",
    "set_output",
  ]);

  let iterations = 0;
  let pendingModeNudge = false;
  let calledOutputTool = false;
  let addedContinueNudge = false;

  while (iterations < MAX_ITERATIONS) {
    iterations++;
    log.info(`» Ollama turn ${iterations}/${MAX_ITERATIONS}...`);

    // Non-streaming with a heartbeat timer so the activity monitor stays alive
    // during long prefill. Streaming was tried but Ollama only emits one tool
    // call per chunk — batched tool calls collapse to one-per-turn, turning a
    // 7-turn run into 26 turns. The heartbeat fires every 60s to prevent the
    // 300s activity timeout from triggering during large-context prefill.
    let response: Awaited<ReturnType<typeof ollama.chat>>;
    const turnStart = Date.now();
    const heartbeat = setInterval(() => {
      log.info(`» still waiting for model... (${Math.round((Date.now() - turnStart) / 1000)}s)`);
    }, 60_000);
    try {
      response = await retry(
        () => ollama.chat({
          model,
          messages,
          tools,
          keep_alive: -1,
          think: false,
          options: { num_ctx: numCtx, temperature: 0.1 },
        }),
        {
          delaysMs: [3_000, 8_000],
          shouldRetry: (err) => {
            const msg = err instanceof Error ? err.message : String(err);
            return /unexpected EOF|XML syntax error|ECONNRESET|ETIMEDOUT|fetch failed/i.test(msg);
          },
          label: `Ollama turn ${iterations}`,
        },
      );
    } catch (err) {
      clearInterval(heartbeat);
      await unloadModel(ollama, model);
      const lastError = err instanceof Error ? err.message : String(err);
      log.error(`Ollama error: ${lastError}`);
      return { success: false, error: `Ollama request failed: ${lastError}` };
    }
    clearInterval(heartbeat);

    const promptTokens = response.prompt_eval_count;
    const evalTokens = response.eval_count;
    const assistantMessage = response.message;

    if (promptTokens !== undefined) {
      const total = promptTokens + (evalTokens ?? 0);
      const pct = Math.round((total / numCtx) * 100);
      log.info(
        `  context: ${promptTokens} prompt + ${evalTokens ?? 0} eval = ${total} tokens (${pct}% of ${numCtx} limit)`,
      );
      if (promptTokens > numCtx * 0.77) {
        messages = pruneToolMessages(messages);
      }
    }

    messages.push(assistantMessage);

    const toolCalls: ToolCall[] | undefined = assistantMessage.tool_calls;

    if (!toolCalls || toolCalls.length === 0) {
      log.debug(`  model text: ${assistantMessage.content?.slice(0, 500)}`);

      // If the model stopped before ever calling an output tool and we haven't
      // nudged yet, give it one more push to continue the workflow — regardless
      // of whether the mode nudge is still pending (the model may have stopped
      // right after select_mode before acting on the guidance).
      if (!calledOutputTool && !addedContinueNudge) {
        log.info(
          "» model stopped before completing task — nudging to continue",
        );
        addedContinueNudge = true;
        messages.push({
          role: "user",
          content:
            "Your task is not complete yet. Continue executing the workflow — " +
            "call the next required tool to finish. " +
            "Do not stop until you have submitted a review (create_pull_request_review) " +
            "or called report_progress with a final summary.",
        });
        continue;
      }

      await unloadModel(ollama, model);

      if (pendingModeNudge) {
        log.info("» agent finished after mode nudge (no tool calls)");
      } else {
        log.info("» agent finished (no tool calls)");
      }

      return {
        success: true,
        output: assistantMessage.content || undefined,
      };
    }

    pendingModeNudge = false;

    const calledSelectMode = toolCalls.some(
      (tc) => tc.function.name === "select_mode",
    );

    for (const toolCall of toolCalls) {
      const toolName = toolCall.function.name;
      const toolArgs = toolCall.function.arguments;

      log.info(`» calling tool: ${toolName}`);
      log.debug(`  args: ${JSON.stringify(toolArgs)}`);

      if (OUTPUT_TOOLS.has(toolName)) {
        calledOutputTool = true;
      }

      if (ctx.onToolUse) {
        ctx.onToolUse({ toolName, input: toolArgs });
      }

      const result = await callMcpTool(
        mcpClient,
        toolName,
        toolArgs as Record<string, unknown>,
      );
      log.debug(`  result: ${result.slice(0, 200)}`);

      messages.push({
        role: "tool",
        content: result,
      });
    }

    // After the FIRST select_mode call, nudge the model to act on the guidance.
    // Only nudge once — repeated nudging causes a loop where the model keeps
    // re-calling select_mode instead of executing the workflow.
    if (calledSelectMode && !pendingModeNudge) {
      pendingModeNudge = true;

      // Parse the selected mode name from the tool result so we can give a
      // more specific first-step instruction.
      let selectedMode = "";
      try {
        const lastToolMsg = messages[messages.length - 1];
        const parsed = JSON.parse(
          typeof lastToolMsg.content === "string" ? lastToolMsg.content : "",
        );
        if (typeof parsed?.modeName === "string")
          selectedMode = parsed.modeName;
      } catch {
        // best-effort
      }

      const firstStep =
        selectedMode === "Review" || selectedMode === "IncrementalReview"
          ? "Your first tool call must be checkout_pr with the PR number from the event context."
          : "Call the first tool required by the workflow now.";

      messages.push({
        role: "user",
        content:
          `Good. You have selected ${selectedMode || "a"} mode and received the workflow. ` +
          "Do NOT call select_mode again. " +
          `${firstStep} ` +
          "Execute the complete workflow step by step until you call create_pull_request_review or report_progress.",
      });
    }
  }

  await unloadModel(ollama, model);

  log.warning(`» agent hit max iterations (${MAX_ITERATIONS})`);

  return {
    success: false,
    error: `Agent exceeded maximum iterations (${MAX_ITERATIONS})`,
  };
}

export const ollamaAgent = agent({
  name: "ollama",
  run: async (ctx: AgentRunContext): Promise<AgentResult> => {
    return runOllamaLoop(ctx);
  },
});