import { tool } from "@opencode-ai/plugin";

const internalBaseUrl =
  process.env.TJWATER_AGENT_INTERNAL_BASE_URL ?? "http://127.0.0.1:8787";
const internalToken = process.env.TJWATER_AGENT_INTERNAL_TOKEN ?? "";
const importDirectory =
  process.env.RESULT_REF_IMPORT_DIR ?? "./data/result-imports";

export default tool({
  description:
    `导入 ${importDirectory} 下的受控 JSON 包装文件并返回 render_ref。文件必须是 { metadata: object, location: { file_path: string }, data: { node_area_map, area_ids?, area_colors? } }，location.file_path 必须与传入的绝对路径完全一致。只接受该目录内的真实文件，不接受目录外路径或指向目录外的符号链接。`,
  args: {
    reason: tool.schema
      .string()
      .describe(
        "为何需要将此本地渲染数据持久化为 render_ref，以便后续通过 render_junctions 渲染到前端。",
      ),
    file_path: tool.schema
      .string()
      .describe(
        `位于 ${importDirectory} 内的包装 JSON 文件绝对路径。必须包含 metadata、location.file_path 和 data；data 才是 render_junctions 使用的 { node_area_map, area_ids?, area_colors? }。`,
      ),
  },
  async execute(args, context) {
    const response = await fetch(
      `${internalBaseUrl}/internal/tools/store-render-ref`,
      {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
          "x-agent-internal-token": internalToken,
        },
        body: JSON.stringify({
          session_id: context.sessionID,
          file_path: args.file_path,
        }),
      },
    );

    const text = await response.text();
    if (!response.ok) {
      throw new Error(text);
    }
    return text;
  },
});