feat: 薄殼原則落地 + seed 下沉 API + MCP 進主庫 + 部署一致性
壓測四橫向問題修正（docs 壓測報告）：
① 薄殼原則成鐵律：能力長在 API，CLI/MCP/lib 只暴露
- seed 下沉成 API 行為：cypher-executor POST /init/seed（一次灌 API+auth recipe），
種子資料移到 server src/lib/api-recipe-seeds.ts，CLI 改薄殼一次呼叫
- 解除 deployFullyOk 連坐 + init 補 seed auth recipe + update 補 seed/全 KV
- registry SUBMISSIONS_KV 補進 REQUIRED_KV_NAMESPACES（修 20/21）
② MCP 統一帳號來源（單一 remote MCP + .env 切 MCP URL）
- MCP 從 sibling repo 搬進 arcrun/mcp/（remote Worker，route 改 mcp.arcrun.dev）
- config 加 mcp_url 三層解析 + getMcpUrl + DEFAULT_MCP_URL
- 新增 acr mcp-setup：依 config 寫專案 .mcp.json（接案切資料夾自動切 MCP）
- acr --version 改動態讀 package.json（根治漂移）
③ Deploy 一致性
- tests/release.feature + scripts/check-release.sh
- local-deploy.sh：CLI npm publish + auto patch bump + CHANGELOG
- local-deploy.sh bash 3.2 相容修正（mapfile / 空陣列 set -u）
- builtins/pnpm-lock.yaml
④ README self-hosted 同步現況（移除 R2 殘留、加 flag/env、多帳號）
CLI bump → 1.3.0
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
uncle6me-web
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/**
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* arcrun_report_feedback — explicit feedback tool for AI agents
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*
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* 對應 SDD .agents/specs/llm-interface/ M1.3
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*
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* AI agent 每次完成 workflow / 卡住 / 解掉問題後 **MUST** call 此 tool。
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* 結構化 issue_type enum 防自由文字難聚合。寫入 KBDB type=agent-feedback block。
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*
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* 後續 M4 weekly_review workflow 聚合這些 block 產出 arcrun-roadmap。
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*
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* 命名注意：M5 全面 rename u6u → arcrun 前，本 tool 直接用新名 arcrun_ prefix
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* 立下範例。其他 u6u_* tool 等 M5 一次切。
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*/
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import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
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import { z } from "zod";
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import { Env } from "../types.js";
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import { kbdbFetch } from "../lib/kbdb-client.js";
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const ISSUE_TYPES = [
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"success_story", // 順利完成，值得記錄這個 pattern
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"doc_unclear", // AGENTS.md / skill / contract 講不清楚
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"tool_missing", // 該有的 MCP tool 沒有
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"error_unhelpful", // 錯誤訊息看不懂下一步
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"unexpected_behavior", // 跟我預期的不一樣
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"feature_request", // 我想要 X 功能
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] as const;
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export function registerReportFeedback(server: McpServer, env: Env, orgNamespace: string) {
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server.tool(
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"arcrun_report_feedback",
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"AI agent 完成 workflow 任務 / 卡住 / 解掉問題後 **必須** call 此 tool 回報。即使順利也要 call (issue_type=success_story)，那是告訴平台「這 pattern 已 work，可推廣」。回饋會寫進 KBDB type=agent-feedback，週報自動聚合產出平台改善 roadmap。",
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{
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issue_type: z.enum(ISSUE_TYPES).describe(
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"回報類型。success_story=順利做完 / doc_unclear=文件不清楚 / tool_missing=該有的 MCP tool 缺 / error_unhelpful=錯誤訊息看不懂下一步 / unexpected_behavior=與預期不符 / feature_request=想要新功能"
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),
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description: z.string().min(10).describe(
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"詳述：你做了什麼、發生什麼、為什麼這算 issue / story。至少 10 字。若是 success_story，描述 pattern 與適用情境"
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),
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workflow_name: z.string().optional().describe("相關 workflow 名稱（若有）"),
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retry_count: z.number().int().min(0).optional().describe("為了搞定，你重試了幾次（含修 YAML / 改參數）"),
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blocked: z.boolean().optional().describe("是否完全擋住（true = 無法繼續），預設 false"),
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suggested_fix: z.string().optional().describe("你建議的修補方向（optional，但很有價值）"),
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agent_user_agent: z.string().optional().describe(
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"你（AI agent）的 client 識別字串。e.g. 'claude-code/1.x'、'cursor-mcp/0.4'、'mira-bot'。讓平台知道哪個 AI 客戶端踩到問題"
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),
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},
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async ({ issue_type, description, workflow_name, retry_count, blocked, suggested_fix, agent_user_agent }) => {
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try {
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if (!env.KBDB) {
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return { content: [{ type: "text", text: "Error: KBDB service binding unavailable" }], isError: true };
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}
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const blockBody = {
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api_key: env.PLATFORM_API_KEY || undefined, // 若 platform key 在，聚集；否則用用戶 namespace
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type: "agent-feedback",
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source: "mcp-tool-call",
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user_id: orgNamespace,
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content: description,
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metadata_json: JSON.stringify({
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issue_type,
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workflow_name,
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retry_count,
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blocked: blocked ?? false,
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suggested_fix,
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agent_user_agent,
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reported_at: new Date().toISOString(),
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}),
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tags_json: JSON.stringify([
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"agent-feedback",
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`issue:${issue_type}`,
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...(blocked ? ["blocked"] : []),
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...(workflow_name ? [`wf:${workflow_name}`] : []),
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]),
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};
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// 走 KBDB service binding（既有 pattern）
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const createResp = await kbdbFetch(env, `/blocks`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify(blockBody),
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});
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if (!createResp.ok) {
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const errBody = await createResp.text();
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return {
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content: [
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{
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type: "text",
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text: JSON.stringify({
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ok: false,
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error_code: "kbdb_write_failed",
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human_message: `回饋寫入 KBDB 失敗：HTTP ${createResp.status}`,
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next_actions: [
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"確認 KBDB 服務在線（試 https://kbdb-get.arcrun.dev/health）",
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"若持續失敗，可暫先在本地記下回饋，稍後重試",
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],
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detail: errBody.slice(0, 200),
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}, null, 2),
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},
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],
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isError: true,
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};
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}
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const data = await createResp.json().catch(() => null);
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return {
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content: [
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{
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type: "text",
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text: JSON.stringify({
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ok: true,
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data: {
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reported: true,
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issue_type,
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block_id: (data as { id?: string } | null)?.id,
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},
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hints: [
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issue_type === "success_story"
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? "感謝記錄成功 pattern！這會被納入週報自動推廣。"
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: "感謝回報！平台週報會聚合這類問題（M4 完成後可看 arcrun-roadmap block）",
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"若還有相關問題（例如同 workflow 不同 issue），可繼續 call",
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],
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}, null, 2),
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},
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],
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};
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} catch (error) {
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return {
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content: [
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{
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type: "text",
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text: JSON.stringify({
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ok: false,
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error_code: "internal_error",
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human_message: `report_feedback 內部錯誤：${error instanceof Error ? error.message : String(error)}`,
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next_actions: ["重試一次", "若持續失敗，請告訴用戶這個 issue 並貼錯誤訊息給 leo"],
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}, null, 2),
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},
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],
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isError: true,
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};
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}
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}
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);
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}
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Reference in New Issue
Block a user