45 lines
1.6 KiB
TypeScript
45 lines
1.6 KiB
TypeScript
import { tool } from "@opencode-ai/plugin";
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const internalBaseUrl =
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process.env.TJWATER_AGENT_INTERNAL_BASE_URL ?? "http://127.0.0.1:8787";
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const internalToken = process.env.TJWATER_AGENT_INTERNAL_TOKEN ?? "";
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export default tool({
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description:
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"将本地 JSON 渲染数据文件存储到受控路径，返回可供 render_junctions 使用的 render_ref（res-...）。前置步骤：先准备好符合 render_junctions 数据结构的 JSON 文件 { node_area_map, area_ids?, area_colors? }，写入本地路径后再调用本工具传入该路径，获取 render_ref 后传给 render_junctions 完成前端渲染。",
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args: {
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reason: tool.schema
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.string()
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.describe(
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"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。",
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),
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file_path: tool.schema
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.string()
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.describe(
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"本地 JSON 文件的绝对路径，内容为 render_junctions 所需的数据结构 { node_area_map, area_ids?, area_colors? }。",
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),
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},
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async execute(args, context) {
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const response = await fetch(
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`${internalBaseUrl}/internal/tools/store-render-ref`,
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{
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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"x-agent-internal-token": internalToken,
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},
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body: JSON.stringify({
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session_id: context.sessionID,
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file_path: args.file_path,
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}),
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},
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);
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const text = await response.text();
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if (!response.ok) {
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throw new Error(text);
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}
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return text;
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},
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});
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