fix(chat): real fixes for thinking-model + URL conventions, not test tweaks

Five real bugs surfaced by the agent-chat smoke against live
qwen3-thinking. None of these are fixed by changing the test — the
test was right to fail.

1. openai-passthrough adapter doubled `/v1` in the request URL. The
   adapter hard-codes `/v1/chat/completions` after the configured base,
   but every OpenAI-compat provider documents its base URL with a
   trailing `/v1` (api.openai.com/v1, llm.example.com/v1, …). Users
   pasting that conventional shape produced
   `https://x/v1/v1/chat/completions` → 404. endpointUrl now strips a
   trailing `/v1` so both forms canonicalize. `/v1beta` (Anthropic-style)
   is preserved.

2. Non-streaming chat returned an empty assistant when thinking models
   (qwen3-thinking, deepseek-reasoner, OpenAI o1) emitted only
   `reasoning_content` with `content: null`. extractChoice now also
   pulls reasoning (every spelling the streaming parser already knows
   about), and a new pickAssistantText helper falls back to it when
   content is empty. A `[response truncated by max_tokens]` marker is
   appended when finish_reason is `length`, so users see the cut-off
   instead of guessing why the answer is short. Symmetric streaming
   fix: the chatStream loop accumulates reasoning and yields ONE
   synthesized `text` frame at the end when content stayed empty,
   keeping the CLI's stdout (which only prints `text` deltas) in sync
   with the persisted thread message.

3. `mcpctl get agent X -o yaml` emitted `kind: public` (the v3
   lifecycle field) instead of `kind: agent` (apply envelope), so
   round-tripping through `apply -f` failed. Same fix shape as the v1
   Llm strip in toApplyDocs — drop kind/status/lastHeartbeatAt/
   inactiveSince/providerSessionId for the agents resource too.

4. Non-streaming `mcpctl chat` printed `thread:<cuid>` (no space) on
   stderr; streaming printed `(thread: <cuid>)` (with space). Tests
   and any other regex watching for one form missed the other.
   Standardize on `thread: <cuid>` (single space) in both paths.

5. agent-chat.smoke's `run()` used `execSync`, which discards stderr on
   success — making any `expect(stderr).toMatch(...)` assertion
   structurally impossible to satisfy in the happy path. Switch to
   `spawnSync` so stderr is actually captured. Includes a small
   shell-style argv splitter so the existing call sites with quoted
   multi-word values (`--system-prompt "..."`) keep working.

Tests: +6 new mcpd unit tests (4 chat-service for the reasoning
fallback / truncation marker / content-preference / streaming synth;
2 llm-adapters for the URL strip + /v1beta preservation). Full mcpd
+ mcplocal + smoke green: 860/860 + 723/723 + 139/139.

src/cli/src/commands/chat.ts

@@ -151,7 +151,10 @@ async function runOneShot(
     const sec = Math.max(0.05, (Date.now() - startMs) / 1000);
     const words = (res.assistant.match(/\S+/g) ?? []).length;
     process.stdout.write(`${res.assistant}\n`);
-    process.stderr.write(styleStats(`(${String(words)}w · ${(words / sec).toFixed(1)} w/s · ${sec.toFixed(1)}s)`) + `  thread:${res.threadId}\n`);
+    // `thread: <id>` — single space after the colon, matching the streaming
+    // path (line 160 below) so any tooling/regex that watches one form picks
+    // up the other too.
+    process.stderr.write(styleStats(`(${String(words)}w · ${(words / sec).toFixed(1)} w/s · ${sec.toFixed(1)}s)`) + `  thread: ${res.threadId}\n`);
     return;
   }
   const bar = installStatusBar();

src/cli/src/commands/get.ts

@@ -408,8 +408,8 @@ function toApplyDocs(resource: string, items: unknown[]): Array<{ kind: string }
   const kind = RESOURCE_KIND[resource] ?? resource;
   return items.map((item) => {
     const cleaned = stripInternalFields(item as Record<string, unknown>);
-    // Llm-specific: the new virtual-provider lifecycle fields collide with
-    // the apply-doc `kind` envelope (the schema uses `kind: public|virtual`)
+    // Llm-specific: the virtual-provider lifecycle fields collide with the
+    // apply-doc `kind` envelope (the schema uses `kind: public|virtual`)
     // and aren't apply-able anyway — they're derived runtime state managed
     // by VirtualLlmService. Drop them so YAML round-trips stay clean.
     if (resource === 'llms') {
@@ -419,6 +419,17 @@ function toApplyDocs(resource: string, items: unknown[]): Array<{ kind: string }
       delete cleaned['inactiveSince'];
       delete cleaned['providerSessionId'];
     }
+    // Agent-specific: same shape as Llm — Agent gained kind/status/etc. in
+    // v3 Stage 1 (virtual agent lifecycle) and the schema-`kind` field
+    // shadows the apply-envelope `kind: agent`. Strip the same set so
+    // `get agent X -o yaml | apply -f -` round-trips without diff.
+    if (resource === 'agents') {
+      delete cleaned['kind'];
+      delete cleaned['status'];
+      delete cleaned['lastHeartbeatAt'];
+      delete cleaned['inactiveSince'];
+      delete cleaned['providerSessionId'];
+    }
     return { kind, ...cleaned };
   });
 }

src/mcpd/src/services/chat.service.ts

@@ -185,6 +185,10 @@ export class ChatService {
           throw new Error(`Adapter returned no choice (status ${String(result.status)})`);
         }
         if (choice.tool_calls !== undefined && choice.tool_calls.length > 0) {
+          // Tool turns: keep `content` literal — even if empty — because the
+          // OpenAI tool-use protocol expects the assistant message to carry
+          // its tool_calls separately from any free-form text. Surfacing
+          // reasoning here would confuse downstream tool dispatchers.
           const assistantTurn = await this.chatRepo.appendMessage({
             threadId: ctx.threadId,
             role: 'assistant',
@@ -219,13 +223,17 @@ export class ChatService {
           await this.chatRepo.updateStatus(assistantTurn.id, 'complete');
           continue;
         }
-        // Terminal text turn.
+        // Terminal text turn. Use pickAssistantText so thinking models that
+        // produced only reasoning_content still yield a usable answer (with
+        // a truncation marker when finish_reason indicates max_tokens
+        // cut-off). Empty body remains empty and bubbles up unchanged.
+        const assistantText = pickAssistantText(choice);
         const finalMsg = await this.chatRepo.appendMessage({
           threadId: ctx.threadId,
           role: 'assistant',
-          content: choice.content ?? '',
+          content: assistantText,
         });
-        assistantFinal = choice.content ?? '';
+        assistantFinal = assistantText;
         lastTurnIndex = finalMsg.turnIndex;
         await this.chatRepo.touchThread(ctx.threadId);
         return { threadId: ctx.threadId, assistant: assistantFinal, turnIndex: lastTurnIndex };
@@ -242,8 +250,16 @@ export class ChatService {
     const ctx = await this.prepareContext(args);
     try {
       for (let i = 0; i < ctx.maxIterations; i += 1) {
-        const accumulated: { content: string; toolCalls: Array<{ id: string; name: string; argumentsJson: string }> } = {
+        // `reasoning` is accumulated alongside `content` so we can fall back
+        // to it when the model produces no `content` (thinking models with a
+        // tight max_tokens, or providers that don't separate the two).
+        const accumulated: {
+          content: string;
+          reasoning: string;
+          toolCalls: Array<{ id: string; name: string; argumentsJson: string }>;
+        } = {
           content: '',
+          reasoning: '',
           toolCalls: [],
         };
         let finishReason: string | null = null;
@@ -257,9 +273,11 @@ export class ChatService {
             yield { type: 'text', delta: evt.contentDelta };
           }
           if (evt.reasoningDelta !== undefined) {
-            // Reasoning is not persisted to the thread (it's the model's
-            // scratchpad, not part of the conversation) — only streamed so
-            // the REPL can show progress while the model thinks.
+            // Streamed live so the REPL can show progress while the model
+            // thinks. Also accumulated so a thinking-only response (no
+            // `content`) still produces a non-empty persisted assistant
+            // turn — see the fallback at the end of this loop iteration.
+            accumulated.reasoning += evt.reasoningDelta;
             yield { type: 'thinking', delta: evt.reasoningDelta };
           }
           if (evt.toolCallDeltas !== undefined) {
@@ -326,10 +344,27 @@ export class ChatService {
           continue;
         }
 
+        // Fall back to reasoning when the model emitted only thinking
+        // output. Mirrors pickAssistantText() in the non-streaming path —
+        // same situation (thinking model + tight max_tokens, or a provider
+        // that bundles the answer into reasoning_content).
+        const persistedContent = pickAssistantText({
+          content: accumulated.content.length > 0 ? accumulated.content : null,
+          ...(accumulated.reasoning.length > 0 ? { reasoning: accumulated.reasoning } : {}),
+          finishReason,
+        });
+        // If we synthesized text from reasoning, yield it as a final `text`
+        // delta so the client's stdout matches what the thread persists.
+        // Without this, the REPL would show only `thinking` deltas (which
+        // the CLI writes to stderr) and stdout would be empty for any
+        // thinking-only response.
+        if (accumulated.content.length === 0 && persistedContent.length > 0) {
+          yield { type: 'text', delta: persistedContent };
+        }
         const finalMsg = await this.chatRepo.appendMessage({
           threadId: ctx.threadId,
           role: 'assistant',
-          content: accumulated.content,
+          content: persistedContent,
         });
         await this.chatRepo.touchThread(ctx.threadId);
         yield { type: 'final', threadId: ctx.threadId, turnIndex: finalMsg.turnIndex };
@@ -682,6 +717,17 @@ export class ChatService {
 
 interface ExtractedChoice {
   content: string | null;
+  /**
+   * Reasoning text emitted by thinking models (qwen3-thinking,
+   * deepseek-reasoner, OpenAI o1 family). Different providers spell the
+   * field differently; the parser accepts every shape the streaming
+   * counterpart already accepts (see `parseStreamingChunk`). When `content`
+   * is null/empty, callers fall back to this so thinking models that
+   * exhaust their token budget on reasoning still produce a usable answer.
+   */
+  reasoning?: string;
+  /** OpenAI's stop reason — `'stop' | 'length' | 'tool_calls' | 'content_filter' | ...`. */
+  finishReason?: string | null;
   tool_calls?: Array<{ id: string; type: 'function'; function: { name: string; arguments: string } }>;
 }
 
@@ -689,17 +735,52 @@ function extractChoice(body: unknown): ExtractedChoice | null {
   if (typeof body !== 'object' || body === null) return null;
   const choices = (body as { choices?: unknown }).choices;
   if (!Array.isArray(choices) || choices.length === 0) return null;
-  const first = choices[0] as { message?: { content?: unknown; tool_calls?: unknown } } | undefined;
+  const first = choices[0] as {
+    message?: {
+      content?: unknown;
+      reasoning_content?: unknown;
+      reasoning?: unknown;
+      provider_specific_fields?: { reasoning_content?: unknown; reasoning?: unknown };
+      tool_calls?: unknown;
+    };
+    finish_reason?: unknown;
+  } | undefined;
   if (first?.message === undefined) return null;
   const content = typeof first.message.content === 'string' ? first.message.content : null;
+  const m = first.message;
+  const reasoning =
+    (typeof m.reasoning_content === 'string' && m.reasoning_content.length > 0 ? m.reasoning_content : undefined)
+    ?? (typeof m.reasoning === 'string' && m.reasoning.length > 0 ? m.reasoning : undefined)
+    ?? (typeof m.provider_specific_fields?.reasoning_content === 'string' && m.provider_specific_fields.reasoning_content.length > 0 ? m.provider_specific_fields.reasoning_content : undefined)
+    ?? (typeof m.provider_specific_fields?.reasoning === 'string' && m.provider_specific_fields.reasoning.length > 0 ? m.provider_specific_fields.reasoning : undefined);
+  const finishReason = typeof first.finish_reason === 'string' ? first.finish_reason : null;
   const toolCalls = first.message.tool_calls;
-  const out: ExtractedChoice = { content };
+  const out: ExtractedChoice = { content, finishReason };
+  if (reasoning !== undefined) out.reasoning = reasoning;
   if (Array.isArray(toolCalls)) {
     out.tool_calls = toolCalls as NonNullable<ExtractedChoice['tool_calls']>;
   }
   return out;
 }
 
+/**
+ * Pick what text to surface (and persist) as the assistant's reply.
+ * Thinking models sometimes emit only `reasoning_content` and leave
+ * `content` null — typically when `max_tokens` is too small for the
+ * thinking budget, but also when the provider configuration just doesn't
+ * separate the two. In that case the reasoning IS the answer for this
+ * request, and the caller should see it. A `length` finish_reason marker
+ * makes truncation visible so users can fix their max_tokens config.
+ */
+function pickAssistantText(choice: ExtractedChoice): string {
+  if (choice.content !== null && choice.content.length > 0) return choice.content;
+  if (choice.reasoning !== undefined && choice.reasoning.length > 0) {
+    const truncated = choice.finishReason === 'length' ? '\n\n[response truncated by max_tokens]' : '';
+    return `${choice.reasoning}${truncated}`;
+  }
+  return '';
+}
+
 function safeParseJson(s: string): unknown {
   if (s === '') return {};
   try {

src/mcpd/src/services/llm/adapters/openai-passthrough.ts

@@ -123,7 +123,15 @@ export class OpenAiPassthroughAdapter implements LlmAdapter {
   }
 
   private endpointUrl(url: string): string {
-    if (url !== '') return url.replace(/\/+$/, '');
+    // Accept both conventional forms users actually paste — base host
+    // (`https://api.openai.com`) and base + version (`https://api.openai.com/v1`).
+    // Every OpenAI-compat provider documents their endpoint with the `/v1`
+    // suffix, so users naturally include it; the adapter then re-appends
+    // `/v1/chat/completions`, producing a doubled-`/v1` 404 against LiteLLM
+    // and others. Strip a trailing `/v1` (with or without slash) so both
+    // shapes resolve to the same canonical base. A more specific suffix
+    // like `/v1beta` is preserved.
+    if (url !== '') return url.replace(/\/+$/, '').replace(/\/v1$/, '');
     const def = DEFAULT_URLS[this.kind];
     if (def === undefined) {
       throw new Error(`${this.kind}: url is required (no default endpoint for this provider)`);

src/mcpd/tests/chat-service.test.ts

@@ -461,6 +461,121 @@ describe('ChatService', () => {
     expect(assistantTurn?.content).not.toContain('Let me think');
   });
 
+  // Regression: thinking models with a tight max_tokens budget produce
+  // `reasoning_content` only and leave `content` null. Without falling back
+  // to reasoning, the assistant turn was empty and the smoke test saw an
+  // empty stdout. This covers BOTH chat() (non-streaming) and chatStream()
+  // (synthetic final text frame so the CLI's stdout matches what's
+  // persisted to the thread).
+  it('chat falls back to reasoning_content when content is null', async () => {
+    const chatRepo = mockChatRepo();
+    const adapter: LlmAdapter = {
+      kind: 'thinking-truncated',
+      infer: vi.fn(async () => ({
+        status: 200,
+        body: {
+          id: 'cmpl-1',
+          object: 'chat.completion',
+          choices: [{
+            index: 0,
+            message: { role: 'assistant', content: null, reasoning_content: 'Thinking out loud about the answer' },
+            finish_reason: 'stop',
+          }],
+        },
+      })),
+      stream: async function*() { yield { data: '[DONE]', done: true }; },
+    };
+    const svc = new ChatService(
+      mockAgents(), mockLlms(), adapterRegistry(adapter),
+      chatRepo, mockPromptRepo(), mockTools(),
+    );
+    const result = await svc.chat({ agentName: 'reviewer', userMessage: 'hi', ownerId: 'owner-1' });
+    expect(result.assistant).toBe('Thinking out loud about the answer');
+    const stored = chatRepo._msgs.find((m) => m.role === 'assistant');
+    expect(stored?.content).toBe('Thinking out loud about the answer');
+  });
+
+  it('chat appends [response truncated by max_tokens] when finish_reason is "length"', async () => {
+    const chatRepo = mockChatRepo();
+    const adapter: LlmAdapter = {
+      kind: 'thinking-clipped',
+      infer: vi.fn(async () => ({
+        status: 200,
+        body: {
+          choices: [{
+            index: 0,
+            message: { role: 'assistant', content: null, reasoning_content: 'partial reasoning that ran out of' },
+            finish_reason: 'length',
+          }],
+        },
+      })),
+      stream: async function*() { yield { data: '[DONE]', done: true }; },
+    };
+    const svc = new ChatService(
+      mockAgents(), mockLlms(), adapterRegistry(adapter),
+      chatRepo, mockPromptRepo(), mockTools(),
+    );
+    const result = await svc.chat({ agentName: 'reviewer', userMessage: 'hi', ownerId: 'owner-1' });
+    expect(result.assistant).toContain('partial reasoning that ran out of');
+    expect(result.assistant).toContain('[response truncated by max_tokens]');
+  });
+
+  it('chat prefers content when both content and reasoning_content are present', async () => {
+    // Thinking models that DO produce content shouldn't see the reasoning
+    // bleed into the response — that's what the streaming path's
+    // text/thinking split is for, and the non-streaming path should match.
+    const chatRepo = mockChatRepo();
+    const adapter: LlmAdapter = {
+      kind: 'thinking-with-content',
+      infer: vi.fn(async () => ({
+        status: 200,
+        body: {
+          choices: [{
+            index: 0,
+            message: { role: 'assistant', content: 'real answer', reasoning_content: 'background thinking' },
+            finish_reason: 'stop',
+          }],
+        },
+      })),
+      stream: async function*() { yield { data: '[DONE]', done: true }; },
+    };
+    const svc = new ChatService(
+      mockAgents(), mockLlms(), adapterRegistry(adapter),
+      chatRepo, mockPromptRepo(), mockTools(),
+    );
+    const result = await svc.chat({ agentName: 'reviewer', userMessage: 'hi', ownerId: 'owner-1' });
+    expect(result.assistant).toBe('real answer');
+    expect(result.assistant).not.toContain('background thinking');
+  });
+
+  it('chatStream emits a synthetic text frame and persists reasoning when content is empty', async () => {
+    const chatRepo = mockChatRepo();
+    const adapter: LlmAdapter = {
+      kind: 'thinking-only-stream',
+      infer: vi.fn(),
+      stream: async function*() {
+        yield { data: JSON.stringify({ choices: [{ delta: { reasoning_content: 'thinking ' }, finish_reason: null }] }) };
+        yield { data: JSON.stringify({ choices: [{ delta: { reasoning_content: 'more.' }, finish_reason: 'stop' }] }) };
+        yield { data: '[DONE]', done: true };
+      },
+    };
+    const svc = new ChatService(
+      mockAgents(), mockLlms(), adapterRegistry(adapter),
+      chatRepo, mockPromptRepo(), mockTools(),
+    );
+    const chunks: Array<{ type: string; delta?: string }> = [];
+    for await (const c of svc.chatStream({ agentName: 'reviewer', userMessage: 'hi', ownerId: 'owner-1' })) {
+      chunks.push({ type: c.type, delta: c.delta });
+    }
+    // 2 thinking deltas (live), 1 synthesized text frame, 1 final.
+    expect(chunks.filter((c) => c.type === 'thinking').map((c) => c.delta)).toEqual(['thinking ', 'more.']);
+    expect(chunks.filter((c) => c.type === 'text').map((c) => c.delta)).toEqual(['thinking more.']);
+    // The thread message captures the synthesized text so resumed chats see
+    // a coherent assistant turn (rather than blank).
+    const stored = chatRepo._msgs.find((m) => m.role === 'assistant');
+    expect(stored?.content).toBe('thinking more.');
+  });
+
   // Regression: provider_specific_fields.reasoning_content shape (LiteLLM
   // passthrough from vLLM) is also recognized.
   it('chatStream recognizes LiteLLM provider_specific_fields.reasoning_content', async () => {

src/mcpd/tests/llm-adapters.test.ts

@@ -71,6 +71,36 @@ describe('OpenAiPassthroughAdapter', () => {
     await expect(adapter.infer(makeCtx())).rejects.toThrow(/no default endpoint/);
   });
 
+  it('infer: strips a trailing /v1 from the configured URL', async () => {
+    // Users naturally paste the OpenAI-style base URL with /v1 because
+    // every provider documents it that way (https://api.openai.com/v1,
+    // https://llm.example.com/v1). The adapter then re-appends
+    // /v1/chat/completions; without normalization this would produce a
+    // doubled-/v1 404 against LiteLLM and friends.
+    const fetchFn = mockFetch([{ match: /\/v1\/chat\/completions$/, status: 200, body: {} }]);
+    const adapter = new OpenAiPassthroughAdapter('openai', { fetch: fetchFn as unknown as typeof fetch });
+    await adapter.infer(makeCtx({ url: 'https://llm.example.com/v1' }));
+    const [url1] = fetchFn.mock.calls[0] as [string];
+    expect(url1).toBe('https://llm.example.com/v1/chat/completions');
+
+    // Trailing slash + /v1 should also normalize correctly.
+    const fetchFn2 = mockFetch([{ match: /\/v1\/chat\/completions$/, status: 200, body: {} }]);
+    const adapter2 = new OpenAiPassthroughAdapter('openai', { fetch: fetchFn2 as unknown as typeof fetch });
+    await adapter2.infer(makeCtx({ url: 'https://llm.example.com/v1/' }));
+    const [url2] = fetchFn2.mock.calls[0] as [string];
+    expect(url2).toBe('https://llm.example.com/v1/chat/completions');
+  });
+
+  it('infer: preserves a trailing /v1beta suffix (only exact /v1 is stripped)', async () => {
+    // Some providers expose `/v1beta` as a parallel API surface — don't
+    // accidentally rewrite that to `/v1` or strip it.
+    const fetchFn = mockFetch([{ match: /\/v1beta\/v1\/chat\/completions$/, status: 200, body: {} }]);
+    const adapter = new OpenAiPassthroughAdapter('openai', { fetch: fetchFn as unknown as typeof fetch });
+    await adapter.infer(makeCtx({ url: 'https://api.example.com/v1beta' }));
+    const [url] = fetchFn.mock.calls[0] as [string];
+    expect(url).toBe('https://api.example.com/v1beta/v1/chat/completions');
+  });
+
   it('infer: omits Authorization when apiKey is empty', async () => {
     const fetchFn = mockFetch([{ match: /ollama/, status: 200, body: {} }]);
     const adapter = new OpenAiPassthroughAdapter('ollama', { fetch: fetchFn as unknown as typeof fetch });

src/mcplocal/tests/smoke/agent-chat.smoke.test.ts

@@ -17,7 +17,7 @@
 import { describe, it, expect, beforeAll, afterAll } from 'vitest';
 import http from 'node:http';
 import https from 'node:https';
-import { execSync } from 'node:child_process';
+import { spawnSync, execSync } from 'node:child_process';
 
 const MCPD_URL = process.env.MCPD_URL ?? 'https://mcpctl.ad.itaz.eu';
 const LLM_URL = process.env.MCPCTL_SMOKE_LLM_URL;
@@ -31,21 +31,37 @@ const AGENT_NAME = `smoke-chat-agent-${SUFFIX}`;
 interface CliResult { code: number; stdout: string; stderr: string }
 
 function run(args: string): CliResult {
-  try {
-    const stdout = execSync(`mcpctl --direct ${args}`, {
-      encoding: 'utf-8',
-      timeout: 60_000,
-      stdio: ['ignore', 'pipe', 'pipe'],
-    });
-    return { code: 0, stdout: stdout.trim(), stderr: '' };
-  } catch (err) {
-    const e = err as { status?: number; stdout?: Buffer | string; stderr?: Buffer | string };
-    return {
-      code: e.status ?? 1,
-      stdout: e.stdout ? (typeof e.stdout === 'string' ? e.stdout : e.stdout.toString('utf-8')) : '',
-      stderr: e.stderr ? (typeof e.stderr === 'string' ? e.stderr : e.stderr.toString('utf-8')) : '',
-    };
+  // spawnSync (not execSync) — execSync returns only stdout on success and
+  // discards stderr, which made any `thread:` assertion against a successful
+  // chat impossible to evaluate. Splitting the args correctly handles the
+  // few existing call sites that quote-wrap multi-word values like
+  // `--system-prompt "You are..."`.
+  const argv = splitArgs(args);
+  const res = spawnSync('mcpctl', ['--direct', ...argv], {
+    encoding: 'utf-8',
+    timeout: 60_000,
+  });
+  return {
+    code: res.status ?? 1,
+    stdout: (res.stdout ?? '').trim(),
+    stderr: (res.stderr ?? '').trim(),
+  };
+}
+
+/**
+ * Tokenize a shell-style argv string with simple double-quote support — just
+ * enough for the smoke test's call shapes. Not a full POSIX parser; we only
+ * need to keep `--system-prompt "You are a smoke test..."` together as one
+ * arg.
+ */
+function splitArgs(s: string): string[] {
+  const out: string[] = [];
+  const re = /"([^"]*)"|(\S+)/g;
+  let m: RegExpExecArray | null;
+  while ((m = re.exec(s)) !== null) {
+    out.push(m[1] !== undefined ? m[1] : (m[2] ?? ''));
   }
+  return out;
 }
 
 function healthz(url: string, timeoutMs = 5000): Promise<boolean> {