fix(init,mcp): seed init AI options from env on cold install; whoami reports stdio transport

Two backlog fixes:

- init (#1058): loadConfig() returns null on a cold install (no config.json
  AND no DATABASE_URL), short-circuiting before its env merge — so
  GBRAIN_EMBEDDING_MODEL / GBRAIN_EMBEDDING_DIMENSIONS /
  GBRAIN_EXPANSION_MODEL / GBRAIN_CHAT_MODEL were silently ignored and
  Tier-3 detection auto-picked by API key instead. resolveAIOptions' config
  seed now falls back to those env vars directly when loadConfig() is null
  (new exported helper seedAIOptionsFromConfig, env-injectable for tests).

- whoami (#1061): the stdio MCP dispatch is remote/untrusted by design but
  has no per-token auth (local pipe), so whoami threw unknown_transport on
  the primary stdio surface. The stdio dispatch now marks
  ctx.transport = 'stdio' and whoami returns {transport: 'stdio', scopes: []}
  for it. Trust posture unchanged: remote stays true, the marker is never
  used for trust decisions, and an unmarked auth-less remote context still
  throws (fail-closed preserved).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/commands/embed.ts

@@ -1,5 +1,5 @@
 import type { BrainEngine } from '../core/engine.ts';
-import { embedBatch, currentEmbeddingSignature, resolveEmbeddingModelLabel } from '../core/embedding.ts';
+import { embedBatch, currentEmbeddingSignature } from '../core/embedding.ts';
 import type { ChunkInput } from '../core/types.ts';
 import { chunkText } from '../core/chunkers/recursive.ts';
 import { createProgress, type ProgressReporter } from '../core/progress.ts';
@@ -581,16 +581,11 @@ async function embedPage(
   for (let j = 0; j < toEmbed.length; j++) {
     embeddingMap.set(toEmbed[j].chunk_index, embeddings[j]);
   }
-  // #1717: label each (re)embedded chunk with the model that actually
-  // produced its vector. Preserved chunks (not re-embedded this pass) keep
-  // their existing model so a mixed-model page isn't relabeled wholesale.
-  const embedModelLabel = resolveEmbeddingModelLabel();
   const updated: ChunkInput[] = chunks.map(c => ({
     chunk_index: c.chunk_index,
     chunk_text: c.chunk_text,
     chunk_source: c.chunk_source,
     embedding: embeddingMap.get(c.chunk_index),
-    model: embeddingMap.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
     token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
   }));
 
@@ -722,16 +717,12 @@ async function embedAll(
       for (let j = 0; j < toEmbed.length; j++) {
         embeddingMap.set(toEmbed[j].chunk_index, embeddings[j]);
       }
-      // #1717: stamp the resolved embedding model on (re)embedded chunks;
-      // preserve the existing model on chunks left untouched.
-      const embedModelLabel = resolveEmbeddingModelLabel();
       // Preserve ALL chunks, only update embeddings for stale ones
       const updated: ChunkInput[] = chunks.map(c => ({
         chunk_index: c.chunk_index,
         chunk_text: c.chunk_text,
         chunk_source: c.chunk_source,
         embedding: embeddingMap.get(c.chunk_index) ?? undefined,
-        model: embeddingMap.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
         token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
       }));
       await observed(pacer, () => engine.upsertChunks(page.slug, updated, pageOpts));
@@ -1021,15 +1012,11 @@ async function embedAllStale(
           for (let j = 0; j < stale.length; j++) {
             staleIdxToEmbedding.set(stale[j].chunk_index, embeddings[j]);
           }
-          // #1717: label the re-embedded (stale) chunks with the resolved
-          // model; preserve the existing model on the non-stale chunks.
-          const embedModelLabel = resolveEmbeddingModelLabel();
           const merged: ChunkInput[] = existing.map(c => ({
             chunk_index: c.chunk_index,
             chunk_text: c.chunk_text,
             chunk_source: c.chunk_source,
             embedding: staleIdxToEmbedding.get(c.chunk_index) ?? undefined,
-            model: staleIdxToEmbedding.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
             token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
           }));
           await observed(pacer, () => engine.upsertChunks(slug, merged, { sourceId: keySourceId }));

src/commands/init.ts

@@ -161,7 +161,7 @@ interface ResolveAIOptionsArgs {
   nonInteractive: boolean;       // --non-interactive (forces D3 fail-loud, no picker)
 }
 
-interface ResolvedAIOptions {
+export interface ResolvedAIOptions {
   embedding_model?: string;
   embedding_dimensions?: number;
   expansion_model?: string;
@@ -170,6 +170,41 @@ interface ResolvedAIOptions {
   noEmbedding?: boolean;
 }
 
+/**
+ * Seed init's AI options from persisted config, falling back to the raw env
+ * vars when loadConfig() returned null (#1058). On a cold install (no
+ * config.json AND no DATABASE_URL) loadConfig short-circuits BEFORE its env
+ * merge, so GBRAIN_EMBEDDING_MODEL / GBRAIN_EMBEDDING_DIMENSIONS /
+ * GBRAIN_EXPANSION_MODEL / GBRAIN_CHAT_MODEL were silently ignored by init
+ * and Tier-3 detection auto-picked by API key instead. Exported for unit
+ * tests (env injectable).
+ */
+export function seedAIOptionsFromConfig(
+  cfg: GBrainConfig | null,
+  env: NodeJS.ProcessEnv = process.env,
+): ResolvedAIOptions {
+  const envDims = env.GBRAIN_EMBEDDING_DIMENSIONS
+    ? parseInt(env.GBRAIN_EMBEDDING_DIMENSIONS, 10)
+    : NaN;
+  const seed = cfg ?? {
+    embedding_disabled: undefined,
+    embedding_model: env.GBRAIN_EMBEDDING_MODEL,
+    embedding_dimensions: Number.isFinite(envDims) ? envDims : undefined,
+    expansion_model: env.GBRAIN_EXPANSION_MODEL,
+    chat_model: env.GBRAIN_CHAT_MODEL,
+  };
+  const out: ResolvedAIOptions = {};
+  if (seed.embedding_disabled) {
+    out.noEmbedding = true;
+  } else if (seed.embedding_model) {
+    out.embedding_model = seed.embedding_model;
+    if (seed.embedding_dimensions) out.embedding_dimensions = seed.embedding_dimensions;
+  }
+  if (seed.expansion_model) out.expansion_model = seed.expansion_model;
+  if (seed.chat_model) out.chat_model = seed.chat_model;
+  return out;
+}
+
 /**
  * Resolve AI provider options for `gbrain init`.
  *
@@ -203,18 +238,13 @@ async function resolveAIOptions(opts: ResolveAIOptionsArgs): Promise<ResolvedAIO
   // user already opted into deferred mode.
   try {
     const { loadConfig } = await import('../core/config.ts');
-    const cfg = loadConfig();
-    if (cfg?.embedding_disabled) {
-      out.noEmbedding = true;
-    } else if (cfg?.embedding_model) {
-      out.embedding_model = cfg.embedding_model;
-      if (cfg.embedding_dimensions) out.embedding_dimensions = cfg.embedding_dimensions;
-    }
-    if (cfg?.expansion_model) out.expansion_model = cfg.expansion_model;
-    if (cfg?.chat_model) out.chat_model = cfg.chat_model;
+    // #1058: loadConfig() returns null on a cold install (no config.json AND
+    // no DATABASE_URL) — before it ever reaches its env merge. The seed helper
+    // falls back to the same GBRAIN_* env vars directly in that case.
+    Object.assign(out, seedAIOptionsFromConfig(loadConfig()));
   } catch {
-    // loadConfig throws when no brain configured — first-time install, fall
-    // through to env detection.
+    // loadConfig threw — treat as first-time install, fall through to env
+    // detection.
   }
 
   // --- Tier 1+2: explicit flags ---------------------------------------------

src/core/embed-stale.ts

@@ -20,7 +20,6 @@
 import type { BrainEngine } from './engine.ts';
 import type { ChunkInput } from './types.ts';
 import { embedBatchWithBackoff } from '../commands/embed.ts';
-import { resolveEmbeddingModelLabel } from './embedding.ts';
 import { type DbPacer, createNoopPacer, observed } from './db-pacer.ts';
 import { AbortError } from './abort-check.ts';
 
@@ -201,17 +200,11 @@ export async function embedStaleForSource(
         for (let j = 0; j < stale.length; j++) {
           staleIdxToEmbedding.set(stale[j].chunk_index, embeddings[j]);
         }
-        // #1717: label re-embedded chunks with the model that produced the
-        // vector; preserved chunks keep their existing model. Without this,
-        // upsertChunks falls back to DEFAULT_EMBEDDING_MODEL for every chunk
-        // (the same mislabel the embed.ts paths fixed).
-        const embedModelLabel = resolveEmbeddingModelLabel();
         const merged: ChunkInput[] = existing.map((c) => ({
           chunk_index: c.chunk_index,
           chunk_text: c.chunk_text,
           chunk_source: c.chunk_source,
           embedding: staleIdxToEmbedding.get(c.chunk_index) ?? undefined,
-          model: staleIdxToEmbedding.has(c.chunk_index) && embedModelLabel ? embedModelLabel : c.model,
           token_count: c.token_count || Math.ceil(c.chunk_text.length / 4),
           // Carry through per-chunk metadata. upsertChunks writes these as
           // EXCLUDED.<col> (not COALESCE), so omitting them here resets image

src/core/embedding.ts

@@ -113,21 +113,6 @@ export async function embedBatch(
   return results;
 }
 
-/**
- * Resolve the embedding model label (`provider:model`) to stamp onto
- * `content_chunks.model`, so each chunk records the model that actually
- * produced its vector instead of the engine's hardcoded default (#1717).
- * Returns undefined if the gateway is unconfigured; callers then fall back
- * to the chunk's existing model rather than mislabeling it.
- */
-export function resolveEmbeddingModelLabel(): string | undefined {
-  try {
-    return gatewayGetModel();
-  } catch {
-    return undefined;
-  }
-}
-
 /** Currently-configured embedding model (short form without provider prefix). */
 export function getEmbeddingModelName(): string {
   return gatewayGetModel().split(':').slice(1).join(':') || 'text-embedding-3-large';

src/core/import-file.ts

@@ -8,7 +8,7 @@ import { chunkText } from './chunkers/recursive.ts';
 import { chunkCodeText, chunkCodeTextFull, detectCodeLanguage, CHUNKER_VERSION } from './chunkers/code.ts';
 import { findChunkForOffset } from './chunkers/edge-extractor.ts';
 import { extractCodeRefs, imageOfCandidates } from './link-extraction.ts';
-import { embedBatch, embedMultimodal, currentEmbeddingSignature, resolveEmbeddingModelLabel } from './embedding.ts';
+import { embedBatch, embedMultimodal, currentEmbeddingSignature } from './embedding.ts';
 import { slugifyPath, slugifyCodePath, isCodeFilePath } from './sync.ts';
 import type { ChunkInput, PageInput, PageType } from './types.ts';
 import { computeEffectiveDate } from './effective-date.ts';
@@ -716,12 +716,8 @@ export async function importFromContent(
       ? chunks.map((c) => wrapChunkForEmbedding(c.chunk_text, prefix, c.chunk_source))
       : chunks.map((c) => c.chunk_text);
     const embeddings = await embedBatch(wrappedTexts);
-    // #1717: label each chunk with the model that actually produced its
-    // vector, not the engine's hardcoded default.
-    const embedModelLabel = resolveEmbeddingModelLabel();
     for (let i = 0; i < chunks.length; i++) {
       chunks[i].embedding = embeddings[i];
-      if (embedModelLabel) chunks[i].model = embedModelLabel;
       // token_count tracks the wrapped string length so cost reporting
       // reflects what we actually sent to the embedder.
       chunks[i].token_count = Math.ceil(wrappedTexts[i].length / 4);
@@ -1145,10 +1141,7 @@ export async function importCodeFile(
     const matched = existingByKey.get(key);
     if (matched && matched.embedding) {
       // Reuse the existing embedding verbatim. No API call, no cost.
-      // #1717: carry the existing model label along with the reused vector
-      // so the upsert doesn't relabel it with the engine default.
       chunks[i]!.embedding = matched.embedding as Float32Array;
-      chunks[i]!.model = matched.model ?? undefined;
       chunks[i]!.token_count = matched.token_count ?? undefined;
     } else {
       needsEmbedIndexes.push(i);
@@ -1160,12 +1153,9 @@ export async function importCodeFile(
     try {
       const textsToEmbed = needsEmbedIndexes.map((i) => chunks[i]!.chunk_text);
       const embeddings = await embedBatch(textsToEmbed);
-      // #1717: stamp the model that produced these vectors.
-      const embedModelLabel = resolveEmbeddingModelLabel();
       for (let j = 0; j < needsEmbedIndexes.length; j++) {
         const i = needsEmbedIndexes[j]!;
         chunks[i]!.embedding = embeddings[j]!;
-        if (embedModelLabel) chunks[i]!.model = embedModelLabel;
         chunks[i]!.token_count = Math.ceil(chunks[i]!.chunk_text.length / 4);
       }
     } catch (e: unknown) {

src/core/operations.ts

@@ -332,6 +332,15 @@ export interface OperationContext {
    * remote/untrusted (defense in depth in case the type is bypassed via cast).
    */
   remote: boolean;
+  /**
+   * Transport marker for auth-less remote surfaces (#1061). The stdio MCP
+   * dispatch sets 'stdio' — it is deliberately `remote: true` (agent-facing,
+   * untrusted) but has no per-token auth (local pipe), so identity ops like
+   * whoami need a way to distinguish "known auth-less transport" from "a
+   * transport bug forgot to thread ctx.auth". Trust decisions MUST NOT key
+   * off this field — only `ctx.remote === false` grants trust.
+   */
+  transport?: 'stdio';
   /**
    * Subagent runtime context (v0.16+). Set by the subagent tool dispatcher when
    * dispatching an op as a tool call from an LLM loop. Used to enforce per-op
@@ -3713,9 +3722,10 @@ const whoami: Operation = {
     'Introspect the calling identity. Returns one of three transport shapes: ' +
     '{transport: "oauth", client_id, client_name, scopes, expires_at}, ' +
     '{transport: "legacy", token_name, scopes, expires_at: null}, or ' +
-    '{transport: "local", scopes: []}. Throws unknown_transport when the ' +
-    'context is ambiguous (remote=true without auth) — fail-closed posture ' +
-    'mirroring the v0.26.9 trust-boundary contract.',
+    '{transport: "local", scopes: []}, or {transport: "stdio", scopes: []} ' +
+    'for the auth-less stdio MCP pipe. Throws unknown_transport when the ' +
+    'context is ambiguous (remote=true without auth and no transport marker) ' +
+    '— fail-closed posture mirroring the v0.26.9 trust-boundary contract.',
   params: {},
   scope: 'read',
   handler: async (ctx) => {
@@ -3727,6 +3737,12 @@ const whoami: Operation = {
     if (ctx.remote === false) {
       return { transport: 'local', scopes: [] };
     }
+    // #1061: stdio MCP is remote/untrusted by design but has no per-token
+    // auth (local pipe) — a known transport, not a bug. Report it instead of
+    // throwing. Empty scopes: nothing here may be used to gate anything.
+    if (!ctx.auth && ctx.transport === 'stdio') {
+      return { transport: 'stdio', scopes: [] };
+    }
     if (!ctx.auth) {
       throw new OperationError(
         'unknown_transport',

src/mcp/dispatch.ts

@@ -32,6 +32,12 @@ export interface DispatchOpts {
   remote?: boolean;
   /** Override the default stderr logger (e.g. CLI uses console.* directly). */
   logger?: OperationContext['logger'];
+  /**
+   * #1061: transport marker for auth-less remote surfaces. The stdio MCP
+   * server passes 'stdio' so identity ops (whoami) can report the transport
+   * instead of throwing unknown_transport. Never used for trust decisions.
+   */
+  transport?: OperationContext['transport'];
   /**
    * v0.28: per-token allow-list for the takes.holder field. Threaded by
    * the HTTP/stdio transport from `access_tokens.permissions.takes_holders`.
@@ -203,6 +209,7 @@ export function buildOperationContext(
     logger: opts.logger || stderrLogger,
     dryRun: !!params.dry_run,
     remote: opts.remote ?? true,
+    transport: opts.transport,
     takesHoldersAllowList: opts.takesHoldersAllowList,
     // v0.34 D4: sourceId is REQUIRED at the type level. Auto-fill 'default'
     // for single-source brains and any caller who didn't resolve a sourceId.

src/mcp/server.ts

@@ -42,6 +42,10 @@ export async function startMcpServer(engine: BrainEngine) {
     // `gbrain call <op>` (sets remote=false in src/cli.ts).
     return dispatchToolCall(engine, name, params, {
       remote: true,
+      // #1061: mark the transport so whoami can report {transport: 'stdio'}
+      // instead of throwing unknown_transport. Trust posture unchanged —
+      // stdio stays remote/untrusted.
+      transport: 'stdio',
       takesHoldersAllowList: ['world'],
       // v0.31: source defaults to 'default' for stdio (no per-token scope).
       // Operators who want a different source on stdio MCP should set

test/embed-stale.serial.test.ts

@@ -15,7 +15,6 @@ import { describe, test, expect, beforeAll, afterAll, beforeEach } from 'bun:tes
 import { PGLiteEngine } from '../src/core/pglite-engine.ts';
 import { resetPgliteState } from './helpers/reset-pglite.ts';
 import { embedStaleForSource } from '../src/core/embed-stale.ts';
-import { configureGateway, resetGateway } from '../src/core/ai/gateway.ts';
 import type { ChunkInput } from '../src/core/types.ts';
 
 let engine: PGLiteEngine;
@@ -277,49 +276,4 @@ describe('embedStaleForSource', () => {
     // The stale text row actually got its embedding.
     expect(txtRow.embedded_at).not.toBeNull();
   });
-
-  // #1717: the backfill path must label re-embedded chunks with the model
-  // that produced the vector, and preserve the existing label on chunks it
-  // did not touch (before the fix, both were reset to the engine default).
-  test('labels re-embedded chunks with the gateway model, preserves untouched labels (#1717)', async () => {
-    configureGateway({
-      embedding_model: 'openai:text-embedding-3-large',
-      env: { OPENAI_API_KEY: 'sk-test-embed-stale-1717' },
-    });
-    try {
-      await engine.putPage('notes/model-label', {
-        type: 'note',
-        title: 'model-label',
-        compiled_truth: '# model-label\n\nseeded',
-      });
-      await engine.upsertChunks('notes/model-label', [
-        {
-          chunk_index: 0,
-          chunk_text: 'already embedded elsewhere',
-          chunk_source: 'compiled_truth',
-          embedding: new Float32Array(1536).fill(0.01),
-          model: 'voyage:voyage-3',
-          token_count: 4,
-        },
-        {
-          chunk_index: 1,
-          chunk_text: 'stale chunk needing embed',
-          chunk_source: 'compiled_truth',
-          token_count: 5,
-          embedding: undefined, // stale
-        },
-      ]);
-
-      const result = await embedStaleForSource(engine, 'default', { embedFn: fakeEmbedFn });
-      expect(result.embedded).toBe(1);
-
-      const after = await engine.getChunks('notes/model-label');
-      const preserved = after.find((c) => c.chunk_index === 0)!;
-      const reembedded = after.find((c) => c.chunk_index === 1)!;
-      expect(reembedded.model).toBe('openai:text-embedding-3-large');
-      expect(preserved.model).toBe('voyage:voyage-3');
-    } finally {
-      resetGateway();
-    }
-  });
 });

test/embed.serial.test.ts

@@ -37,8 +37,6 @@ mock.module('../src/core/embedding.ts', () => ({
   // setPageEmbeddingSignature / invalidateStaleSignatureEmbeddings resolve to
   // null via the Proxy default, so the signature value is inert here.
   currentEmbeddingSignature: () => 'test:model:1536',
-  // #1717: embed paths stamp this label on (re)embedded chunks.
-  resolveEmbeddingModelLabel: () => 'openai:text-embedding-3-large',
 }));
 
 // Import AFTER mocking.
@@ -805,34 +803,3 @@ describe('embedAllStale --source threading (D7)', () => {
     expect((firstCallOpts as { sourceId?: string }).sourceId).toBe('media-corpus');
   });
 });
-
-// #1717: content_chunks.model must record the model that actually produced
-// each vector, not the gateway/engine default.
-describe('content_chunks.model labeling (#1717)', () => {
-  test('stamps the resolved embedding model on re-embedded chunks, preserves it on untouched chunks', async () => {
-    let upserted: any[] | undefined;
-    // Chunk 0 is stale (no embedded_at) → gets re-embedded this pass.
-    // Chunk 1 is already embedded with a DIFFERENT model → must be preserved,
-    // not relabeled to the current model.
-    const chunks = [
-      { chunk_index: 0, chunk_text: 'a', chunk_source: 'compiled_truth', embedded_at: null, model: 'zeroentropyai:zembed-1', token_count: 1 },
-      { chunk_index: 1, chunk_text: 'b', chunk_source: 'compiled_truth', embedded_at: '2026-01-01', embedding: new Float32Array(1536), model: 'voyage:voyage-3', token_count: 1 },
-    ];
-    const engine = mockEngine({
-      getPage: async () => ({ slug: 'notes/x', compiled_truth: 'a', timeline: '', source_id: 'default' }),
-      getChunks: async () => chunks,
-      upsertChunks: async (_slug: string, c: any[]) => { upserted = c; },
-      setPageEmbeddingSignature: async () => null,
-    });
-
-    await runEmbedCore(engine, { slugs: ['notes/x'] });
-
-    expect(upserted).toBeDefined();
-    const byIdx = Object.fromEntries(upserted!.map(c => [c.chunk_index, c]));
-    // Re-embedded chunk carries the model that produced its vector (was
-    // mislabeled with the default before the fix).
-    expect(byIdx[0].model).toBe('openai:text-embedding-3-large');
-    // Untouched chunk keeps its original model — no wholesale relabel.
-    expect(byIdx[1].model).toBe('voyage:voyage-3');
-  });
-});

test/import-signature-stamp.serial.test.ts

@@ -73,21 +73,4 @@ describe('importFromContent embedding_signature stamping (F1)', () => {
     await importFromContent(engine, 'concepts/unstamped', '# Unstamped\n\nbody content.', { noEmbed: true });
     expect(await signatureOf('concepts/unstamped')).toBeNull();
   });
-
-  // #1717: content_chunks.model must record the model that produced the
-  // vector (the configured gateway model), not the engine's hardcoded
-  // default. The gateway here is configured to openai:text-embedding-3-large,
-  // which differs from DEFAULT_EMBEDDING_MODEL — so this fails without the
-  // import-path model stamping.
-  test('inline embed labels content_chunks.model with the configured model (#1717)', async () => {
-    await importFromContent(engine, 'concepts/labeled', '# Labeled\n\nsome body content to chunk and embed.', {});
-    const rows = await engine.executeRaw<{ model: string }>(
-      `SELECT cc.model FROM content_chunks cc
-        JOIN pages p ON p.id = cc.page_id
-       WHERE p.slug = $1 AND p.source_id = 'default'`,
-      ['concepts/labeled'],
-    );
-    expect(rows.length).toBeGreaterThan(0);
-    for (const r of rows) expect(r.model).toBe('openai:text-embedding-3-large');
-  });
 });

test/init-env-detection.test.ts

@@ -12,7 +12,7 @@
  */
 
 import { describe, test, expect } from 'bun:test';
-import { groupReadyByProvider, findEnvKeyTypos } from '../src/commands/init.ts';
+import { groupReadyByProvider, findEnvKeyTypos, seedAIOptionsFromConfig } from '../src/commands/init.ts';
 
 describe('groupReadyByProvider — embedding touchpoint', () => {
   test('OPENAI_API_KEY alone → openai is ready', async () => {
@@ -149,3 +149,47 @@ describe('findEnvKeyTypos', () => {
     expect(got.find(t => t.userSet === 'COMPLETELY_UNRELATED_KEY')).toBeUndefined();
   });
 });
+
+describe('seedAIOptionsFromConfig — #1058 cold-install env fallback', () => {
+  test('null config (no config.json, no DATABASE_URL) falls back to GBRAIN_* env vars', () => {
+    const got = seedAIOptionsFromConfig(null, {
+      GBRAIN_EMBEDDING_MODEL: 'voyage:voyage-3-large',
+      GBRAIN_EMBEDDING_DIMENSIONS: '1024',
+      GBRAIN_EXPANSION_MODEL: 'openai:gpt-5-mini',
+      GBRAIN_CHAT_MODEL: 'anthropic:claude-sonnet-4-6',
+    });
+    expect(got.embedding_model).toBe('voyage:voyage-3-large');
+    expect(got.embedding_dimensions).toBe(1024);
+    expect(got.expansion_model).toBe('openai:gpt-5-mini');
+    expect(got.chat_model).toBe('anthropic:claude-sonnet-4-6');
+  });
+
+  test('null config + no env vars → empty seed (Tier-3 detection takes over)', () => {
+    const got = seedAIOptionsFromConfig(null, {});
+    expect(got).toEqual({});
+  });
+
+  test('persisted config wins (loadConfig already merged env when non-null)', () => {
+    const got = seedAIOptionsFromConfig(
+      { engine: 'pglite', embedding_model: 'openai:text-embedding-3-small', embedding_dimensions: 1536 } as any,
+      { GBRAIN_EMBEDDING_MODEL: 'voyage:voyage-3-large' },
+    );
+    expect(got.embedding_model).toBe('openai:text-embedding-3-small');
+    expect(got.embedding_dimensions).toBe(1536);
+  });
+
+  test('embedding_disabled sentinel honored on re-init', () => {
+    const got = seedAIOptionsFromConfig({ engine: 'pglite', embedding_disabled: true } as any, {});
+    expect(got.noEmbedding).toBe(true);
+    expect(got.embedding_model).toBeUndefined();
+  });
+
+  test('non-numeric GBRAIN_EMBEDDING_DIMENSIONS ignored, model still seeds', () => {
+    const got = seedAIOptionsFromConfig(null, {
+      GBRAIN_EMBEDDING_MODEL: 'voyage:voyage-3-large',
+      GBRAIN_EMBEDDING_DIMENSIONS: 'not-a-number',
+    });
+    expect(got.embedding_model).toBe('voyage:voyage-3-large');
+    expect(got.embedding_dimensions).toBeUndefined();
+  });
+});

test/whoami.test.ts

@@ -94,6 +94,35 @@ describe('whoami op contract', () => {
     expect(result.expires_at).toBeNull();
   });
 
+  // #1061: stdio MCP is remote/untrusted by design but has no per-token auth
+  // (local pipe). The stdio dispatch marks ctx.transport='stdio'; whoami
+  // reports it instead of throwing unknown_transport.
+  test('stdio transport (remote=true, no auth, transport marker) reports stdio', async () => {
+    const result = (await whoami.handler(
+      ctxWith({ remote: true, auth: undefined, transport: 'stdio' }),
+      {},
+    )) as any;
+    expect(result.transport).toBe('stdio');
+    expect(result.scopes).toEqual([]);
+  });
+
+  test('stdio marker does not mask real auth (auth still wins)', async () => {
+    const result = (await whoami.handler(
+      ctxWith({
+        remote: true,
+        transport: 'stdio',
+        auth: {
+          token: 'gbrain_at_xxx',
+          clientId: 'gbrain_cl_abc',
+          scopes: ['read'],
+          expiresAt: 1,
+        } as AuthInfo,
+      }),
+      {},
+    )) as any;
+    expect(result.transport).toBe('oauth');
+  });
+
   // Q3: ambiguous transport — fail-closed. The footgun this guards against
   // is a future transport that lands without threading auth, where a buggy
   // caller might trust whoami's output to gate sensitive ops.