chore: merge dev into v2 (#34788)

Co-authored-by: Brendan Allan <14191578+Brendonovich@users.noreply.github.com>
Co-authored-by: Kit Langton <kit.langton@gmail.com>
Co-authored-by: opencode-agent[bot] <opencode-agent[bot]@users.noreply.github.com>
Co-authored-by: Affan Ali <93028901+affanali2k3@users.noreply.github.com>
Co-authored-by: affanali2k3 <affanalikhanxx@gmail.com>
Co-authored-by: Frank <frank@anoma.ly>
Co-authored-by: opencode-agent[bot] <219766164+opencode-agent[bot]@users.noreply.github.com>
Co-authored-by: 𝓛𝓲𝓽𝓽𝓵𝓮 𝓕𝓻𝓪𝓷𝓴 <little-frank@opencord.local>
Co-authored-by: Aiden Cline <63023139+rekram1-node@users.noreply.github.com>
Co-authored-by: Jay V <air@live.ca>
Co-authored-by: Dax Raad <d@ironbay.co>
Co-authored-by: Aarav Sareen <96787824+arvsrn@users.noreply.github.com>
Co-authored-by: OpeOginni <107570612+OpeOginni@users.noreply.github.com>
Co-authored-by: Luke Parker <10430890+Hona@users.noreply.github.com>
Co-authored-by: Ben Guthrie <benjee.012@gmail.com>
Co-authored-by: Dax <mail@thdxr.com>
Co-authored-by: Filip <34747899+neriousy@users.noreply.github.com>
Co-authored-by: Max Anderson <max.a.anderson95@gmail.com>
Co-authored-by: Brendan Allan <git@brendonovich.dev>
Co-authored-by: Jack <jack@anoma.ly>
Co-authored-by: Shoubhit Dash <shoubhit2005@gmail.com>
Co-authored-by: Dustin Deus <deusdustin@gmail.com>
Co-authored-by: starptech <starptech@starptechs-MBP.fritz.box>
Co-authored-by: Aiden Cline <aidenpcline@gmail.com>
Co-authored-by: usrnk1 <7547651+usrnk1@users.noreply.github.com>
Co-authored-by: Jay <53023+jayair@users.noreply.github.com>
Co-authored-by: runvip <164729189+runvip@users.noreply.github.com>
Co-authored-by: opencode <opencode@sst.dev>
Co-authored-by: Julian Coy <julian@ex-machina.co>
Co-authored-by: Vladimir Glafirov <vglafirov@gitlab.com>

packages/llm/test/adapter.test.ts

@@ -1,6 +1,6 @@
 import { describe, expect } from "bun:test"
 import { Effect, Schema, Stream } from "effect"
-import { LLM } from "../src"
+import { LLM, LLMResponse } from "../src"
 import { Route, Endpoint, LLMClient, Protocol, type FramingDef } from "../src/route"
 import { Model } from "../src/schema"
 import { testEffect } from "./lib/effect"
@@ -112,9 +112,16 @@ describe("llm route", () => {
       const llm = yield* LLMClient.Service
       const events = Array.from(yield* llm.stream(request).pipe(Stream.runCollect))
       const response = yield* llm.generate(request)
+      const reduced = LLMResponse.fromEvents(events)
 
       expect(events.map((event) => event.type)).toEqual(["text-delta", "finish"])
-      expect(response.events.map((event) => event.type)).toEqual(["text-delta", "finish"])
+      expect(reduced).toBeDefined()
+      if (!reduced) throw new Error("stream reducer did not produce a completed response")
+      expect(response.events).toEqual(events)
+      expect(response.message).toEqual(reduced.message)
+      expect(response.usage).toEqual(reduced.usage)
+      expect(response.finishReason).toEqual(reduced.finishReason)
+      expect(response.message.content).toEqual([{ type: "text", text: 'echo:{"body":"hello"}' }])
     }),
   )
 

packages/llm/test/fixtures/recordings/gemini-cache/reports-cachedcontenttokencount-on-identical-second-call.json

@@ -21,7 +21,7 @@
         "headers": {
           "content-type": "text/event-stream"
         },
-        "body": ""
+        "body": "data: {\"candidates\":[{\"content\":{\"role\":\"model\",\"parts\":[{\"text\":\"Hi.\"}]},\"finishReason\":\"STOP\"}],\"usageMetadata\":{\"promptTokenCount\":1200,\"candidatesTokenCount\":2,\"totalTokenCount\":1202}}\n\n"
       }
     },
     {
@@ -39,7 +39,7 @@
         "headers": {
           "content-type": "text/event-stream"
         },
-        "body": ""
+        "body": "data: {\"candidates\":[{\"content\":{\"role\":\"model\",\"parts\":[{\"text\":\"Hi.\"}]},\"finishReason\":\"STOP\"}],\"usageMetadata\":{\"cachedContentTokenCount\":1100,\"promptTokenCount\":1200,\"candidatesTokenCount\":2,\"totalTokenCount\":1202}}\n\n"
       }
     }
   ]

packages/llm/test/llm.test.ts

@@ -90,15 +90,47 @@ describe("llm constructors", () => {
       provider: "fake",
       route: chatRoute,
     })
-    const updated = Model.update(base, { route: responsesRoute })
+    const updated = Model.update(base, {
+      route: responsesRoute,
+      defaults: { generation: { maxTokens: 20 } },
+      compatibility: { toolSchema: "gemini" },
+    })
+    const updatedInput = Model.input(updated)
 
     expect(updated).toBeInstanceOf(Model)
     expect(String(updated.id)).toBe("fake-model")
     expect(updated.route).toBe(responsesRoute)
-    expect(String(Model.input(updated).provider)).toBe("fake")
+    expect(updated.defaults?.generation).toEqual({ maxTokens: 20 })
+    expect(updated.compatibility).toEqual({ toolSchema: "gemini" })
+    expect(updatedInput.defaults).toBe(updated.defaults)
+    expect(updatedInput.compatibility).toBe(updated.compatibility)
+    expect(String(updatedInput.provider)).toBe("fake")
     expect(Model.update(updated, {})).toBe(updated)
   })
 
+  test("carries model defaults and compatibility through route model selection", () => {
+    const model = chatRoute.model({
+      id: "kimi-k2",
+      defaults: {
+        limits: { context: 128_000, output: 8_192 },
+        generation: { maxTokens: 1_024, stop: ["END"] },
+        providerOptions: { openai: { parallelToolCalls: false } },
+        http: { body: { extra_body: true } },
+      },
+      compatibility: { toolSchema: "moonshot" },
+    })
+    const request = LLM.request({ model, prompt: "Say hello." })
+
+    expect(request.model.defaults?.limits).toEqual({ context: 128_000, output: 8_192 })
+    expect(request.model.defaults?.generation).toEqual({ maxTokens: 1_024, stop: ["END"] })
+    expect(request.model.defaults?.providerOptions).toEqual({ openai: { parallelToolCalls: false } })
+    expect(request.model.defaults?.http).toEqual({ body: { extra_body: true } })
+    expect(request.model.compatibility).toEqual({ toolSchema: "moonshot" })
+    expect(request.generation).toBeUndefined()
+    expect(request.providerOptions).toBeUndefined()
+    expect(request.http).toBeUndefined()
+  })
+
   test("builds tool choices from names and tools", () => {
     const tool = ToolDefinition.make({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })
 

packages/llm/test/prepare.test.ts

@@ -0,0 +1,178 @@
+import { describe, expect, test } from "bun:test"
+import { Effect, Schema } from "effect"
+import { HttpClientRequest } from "effect/unstable/http"
+import { LLM, mergeProviderOptions } from "../src"
+import { AnthropicMessages, OpenAIChat } from "../src/protocols"
+import { Auth, LLMClient } from "../src/route"
+import { it } from "./lib/effect"
+import { dynamicResponse } from "./lib/http"
+import { deltaChunk } from "./lib/openai-chunks"
+import { sseEvents } from "./lib/sse"
+
+const TargetJson = Schema.fromJsonString(Schema.Unknown)
+const decodeJson = Schema.decodeUnknownSync(TargetJson)
+
+describe("request option precedence", () => {
+  test("deep-merges provider option records and replaces arrays, primitives, and null", () => {
+    const merged = mergeProviderOptions(
+      {
+        openai: {
+          include: ["route"],
+          metadata: { route: true, shared: "route" },
+          nullable: "route",
+          primitive: "route",
+        },
+      },
+      {
+        openai: {
+          include: ["model"],
+          metadata: { model: true, shared: "model" },
+          nullable: null,
+          primitive: "model",
+        },
+      },
+      { openai: { metadata: { request: true }, primitive: false } },
+    )
+
+    expect(merged).toEqual({
+      openai: {
+        include: ["model"],
+        metadata: { route: true, model: true, request: true, shared: "model" },
+        nullable: null,
+        primitive: false,
+      },
+    })
+  })
+
+  it.effect("prepares bodies with route defaults, model defaults, and call options in order", () =>
+    Effect.gen(function* () {
+      const route = OpenAIChat.route.with({
+        endpoint: { baseURL: "https://api.openai.test/v1/" },
+        auth: Auth.bearer("test"),
+        generation: { maxTokens: 10, temperature: 1, stop: ["route"] },
+        providerOptions: { openai: { store: false, reasoningEffort: "low" } },
+      })
+      const model = route.model({
+        id: "gpt-4o-mini",
+        defaults: {
+          generation: { maxTokens: 20, temperature: 0.5, frequencyPenalty: 0.25, stop: ["model"] },
+          providerOptions: { openai: { reasoningEffort: "medium" } },
+        },
+      })
+      const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
+        LLM.request({
+          model,
+          prompt: "Say hello.",
+          generation: { maxTokens: 30, topP: 0.9, stop: ["request"] },
+          providerOptions: { openai: { store: true } },
+        }),
+      )
+
+      expect(prepared.body).toMatchObject({
+        model: "gpt-4o-mini",
+        stream: true,
+        max_tokens: 30,
+        temperature: 0.5,
+        top_p: 0.9,
+        frequency_penalty: 0.25,
+        store: true,
+        reasoning_effort: "medium",
+      })
+      expect(prepared.body.stop).toEqual(["request"])
+    }),
+  )
+
+  it.effect("applies model HTTP defaults before request HTTP overlays", () =>
+    LLMClient.generate(
+      LLM.request({
+        model: OpenAIChat.route
+          .with({
+            endpoint: { baseURL: "https://api.openai.test/v1/" },
+            auth: Auth.bearer("fresh-key"),
+            http: {
+              body: { metadata: { route: true, shared: "route" }, value: "route" },
+              headers: { "x-route": "route", "x-shared": "route" },
+              query: { route: "1", shared: "route" },
+            },
+          })
+          .model({
+            id: "gpt-4o-mini",
+            defaults: {
+              http: {
+                body: { metadata: { model: true, shared: "model" }, value: "model" },
+                headers: { "x-model": "model", "x-shared": "model" },
+                query: { model: "1", shared: "model" },
+              },
+            },
+          }),
+        prompt: "Say hello.",
+        http: {
+          body: { metadata: { request: true }, value: null },
+          headers: { "x-request": "request" },
+          query: { request: "1" },
+        },
+      }),
+    ).pipe(
+      Effect.provide(
+        dynamicResponse((input) =>
+          Effect.gen(function* () {
+            const web = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
+            expect(web.url).toBe("https://api.openai.test/v1/chat/completions?route=1&shared=model&model=1&request=1")
+            expect(web.headers.get("authorization")).toBe("Bearer fresh-key")
+            expect(web.headers.get("x-route")).toBe("route")
+            expect(web.headers.get("x-model")).toBe("model")
+            expect(web.headers.get("x-request")).toBe("request")
+            expect(web.headers.get("x-shared")).toBe("model")
+            expect(decodeJson(input.text)).toMatchObject({
+              metadata: { route: true, model: true, request: true, shared: "model" },
+              value: null,
+            })
+            return input.respond(sseEvents(deltaChunk({}, "stop")), {
+              headers: { "content-type": "text/event-stream" },
+            })
+          }),
+        ),
+      ),
+    ),
+  )
+
+  it.effect("rejects raw body overlays for protocol-owned roots", () =>
+    Effect.gen(function* () {
+      const model = OpenAIChat.route
+        .with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
+        .model({ id: "gpt-4o-mini" })
+      const error = yield* LLMClient.prepare(
+        LLM.request({
+          model,
+          prompt: "Say hello.",
+          http: { body: { model: "gpt-5", messages: [], tools: [] } },
+        }),
+      ).pipe(Effect.flip)
+
+      expect(error.reason).toMatchObject({
+        _tag: "InvalidRequest",
+        message: "http.body cannot overlay protocol-owned field(s): model, messages, tools",
+      })
+    }),
+  )
+
+  it.effect("uses model output limits after route limits and before call maxTokens", () =>
+    Effect.gen(function* () {
+      const route = AnthropicMessages.route.with({
+        endpoint: { baseURL: "https://api.anthropic.test/v1/" },
+        auth: Auth.header("x-api-key", "test"),
+        limits: { output: 128 },
+      })
+      const model = route.model({ id: "claude-sonnet-4-5", defaults: { limits: { output: 64 } } })
+      const withoutMaxTokens = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
+        LLM.request({ model, prompt: "Say hello.", cache: "none" }),
+      )
+      const withMaxTokens = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
+        LLM.request({ model, prompt: "Say hello.", cache: "none", generation: { maxTokens: 32 } }),
+      )
+
+      expect(withoutMaxTokens.body.max_tokens).toBe(64)
+      expect(withMaxTokens.body.max_tokens).toBe(32)
+    }),
+  )
+})

packages/llm/test/provider/anthropic-messages.test.ts

@@ -395,6 +395,10 @@ describe("Anthropic Messages route", () => {
       expect(response.events.find((event) => event.type === "reasoning-end")).toMatchObject({
         providerMetadata: { anthropic: { signature: "sig_1" } },
       })
+      expect(response.message.content).toEqual([
+        { type: "text", text: "Hello!" },
+        { type: "reasoning", text: "thinking", providerMetadata: { anthropic: { signature: "sig_1" } } },
+      ])
       expect(response.events.at(-1)).toMatchObject({
         type: "finish",
         reason: "stop",

packages/llm/test/provider/openai-chat.test.ts

@@ -1,7 +1,7 @@
 import { describe, expect } from "bun:test"
 import { Effect, Schema, Stream } from "effect"
 import { HttpClientRequest } from "effect/unstable/http"
-import { LLM, LLMError, Message, Model, ToolCallPart, Usage } from "../../src"
+import { LLM, LLMError, LLMEvent, Message, Model, ToolCallPart, Usage } from "../../src"
 import * as Azure from "../../src/providers/azure"
 import * as OpenAI from "../../src/providers/openai"
 import * as OpenAIChat from "../../src/protocols/openai-chat"
@@ -597,19 +597,22 @@ describe("OpenAI Chat route", () => {
         }),
         deltaChunk({ tool_calls: [{ index: 0, function: { arguments: ':"weather"}' } }] }),
       )
-      const response = yield* LLMClient.generate(
-        LLM.updateRequest(request, {
-          tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
-        }),
-      ).pipe(Effect.provide(fixedResponse(body)))
+      const input = LLM.updateRequest(request, {
+        tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
+      })
+      const events = Array.from(
+        yield* LLMClient.stream(input).pipe(Stream.runCollect, Effect.provide(fixedResponse(body))),
+      )
+      const error = yield* LLMClient.generate(input).pipe(Effect.provide(fixedResponse(body)), Effect.flip)
 
-      expect(response.events).toEqual([
+      expect(events).toEqual([
         { type: "step-start", index: 0 },
         { type: "tool-input-start", id: "call_1", name: "lookup", providerMetadata: undefined },
         { type: "tool-input-delta", id: "call_1", name: "lookup", text: '{"query"' },
         { type: "tool-input-delta", id: "call_1", name: "lookup", text: ':"weather"}' },
       ])
-      expect(response.toolCalls).toEqual([])
+      expect(events.filter(LLMEvent.is.toolCall)).toEqual([])
+      expect(error.message).toContain("Provider stream ended without a terminal finish event")
     }),
   )
 

packages/llm/test/provider/openai-responses.test.ts

@@ -778,6 +778,11 @@ describe("OpenAI Responses route", () => {
         { type: "step-finish", index: 0, reason: "stop" },
         { type: "finish", reason: "stop" },
       ])
+      expect(response.events.filter((event) => event.type === "finish")).toHaveLength(1)
+      expect(response.message.content).toEqual([
+        { type: "reasoning", text: "thinking" },
+        { type: "text", text: "Hello" },
+      ])
     }),
   )
 

packages/llm/test/response.test.ts

@@ -0,0 +1,98 @@
+import { describe, expect, test } from "bun:test"
+import { LLMEvent, LLMResponse } from "../src"
+
+const reduce = (events: ReadonlyArray<LLMEvent>) => events.reduce(LLMResponse.reduce, LLMResponse.empty())
+const finishEvents = (events: ReadonlyArray<LLMEvent>) => events.filter(LLMEvent.is.finish)
+
+describe("LLMResponse reducer", () => {
+  test("assembles interleaved reasoning and text with end metadata", () => {
+    const events = [
+      LLMEvent.reasoningStart({ id: "r1" }),
+      LLMEvent.reasoningDelta({ id: "r1", text: "I should " }),
+      LLMEvent.textStart({ id: "t1" }),
+      LLMEvent.reasoningDelta({ id: "r1", text: "compare..." }),
+      LLMEvent.reasoningEnd({ id: "r1", providerMetadata: { anthropic: { signature: "sig" } } }),
+      LLMEvent.textDelta({ id: "t1", text: "Answer" }),
+      LLMEvent.textEnd({ id: "t1" }),
+      LLMEvent.finish({ reason: "stop", usage: { outputTokens: 5 } }),
+    ]
+    const response = LLMResponse.fromEvents(events)
+
+    expect(response?.finishReason).toBe("stop")
+    expect(response?.usage).toMatchObject({ outputTokens: 5 })
+    expect(response?.events).toEqual(events)
+    expect(response?.events.map((event) => event.type)).toEqual([
+      "reasoning-start",
+      "reasoning-delta",
+      "text-start",
+      "reasoning-delta",
+      "reasoning-end",
+      "text-delta",
+      "text-end",
+      "finish",
+    ])
+    expect(finishEvents(response?.events ?? [])).toHaveLength(1)
+    expect(response?.message.content).toEqual([
+      {
+        type: "reasoning",
+        text: "I should compare...",
+        providerMetadata: { anthropic: { signature: "sig" } },
+      },
+      { type: "text", text: "Answer" },
+    ])
+  })
+
+  test("preserves partial content without completing a failed stream", () => {
+    const state = reduce([LLMEvent.textStart({ id: "t1" }), LLMEvent.textDelta({ id: "t1", text: "partial" })])
+
+    expect(LLMResponse.complete(state)).toBeUndefined()
+    expect(state.message.content).toEqual([{ type: "text", text: "partial" }])
+  })
+
+  test("does not complete ended content without a terminal finish", () => {
+    const state = reduce([
+      LLMEvent.textStart({ id: "t1" }),
+      LLMEvent.textDelta({ id: "t1", text: "partial" }),
+      LLMEvent.textEnd({ id: "t1" }),
+    ])
+
+    expect(LLMResponse.complete(state)).toBeUndefined()
+    expect(state.message.content).toEqual([{ type: "text", text: "partial" }])
+  })
+
+  test("uses terminal usage when present and keeps prior usage when finish omits it", () => {
+    const withFinishUsage = LLMResponse.fromEvents([
+      LLMEvent.stepFinish({ index: 0, reason: "stop", usage: { inputTokens: 3 } }),
+      LLMEvent.finish({ reason: "stop", usage: { outputTokens: 2 } }),
+    ])
+    const withoutFinishUsage = LLMResponse.fromEvents([
+      LLMEvent.stepFinish({ index: 0, reason: "stop", usage: { inputTokens: 3 } }),
+      LLMEvent.finish({ reason: "stop" }),
+    ])
+
+    expect(withFinishUsage?.usage).toMatchObject({ outputTokens: 2 })
+    expect(withoutFinishUsage?.usage).toMatchObject({ inputTokens: 3 })
+  })
+
+  test("assembles tool-call content only after the completed tool call event", () => {
+    const pending = reduce([
+      LLMEvent.toolInputStart({ id: "call_1", name: "lookup" }),
+      LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: '{"query"' }),
+    ])
+
+    expect(pending.message.content).toEqual([])
+    expect(pending.toolInputs.call_1?.text).toBe('{"query"')
+
+    const response = LLMResponse.fromEvents([
+      ...pending.events,
+      LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: ':"weather"}' }),
+      LLMEvent.toolInputEnd({ id: "call_1", name: "lookup" }),
+      LLMEvent.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } }),
+      LLMEvent.finish({ reason: "tool-calls" }),
+    ])
+
+    expect(response?.message.content).toEqual([
+      { type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
+    ])
+  })
+})

packages/llm/test/tool-schema-projection.test.ts

@@ -0,0 +1,117 @@
+import { describe, expect, test } from "bun:test"
+import { Effect } from "effect"
+import { LLM } from "../src"
+import { OpenAIChat } from "../src/protocols"
+import { ToolSchemaProjection } from "../src/protocols/utils/tool-schema"
+import { Auth, LLMClient } from "../src/route"
+import { it } from "./lib/effect"
+
+describe("tool schema projections", () => {
+  test("moonshot strips $ref siblings and converts tuple arrays to a schema object", () => {
+    expect(
+      ToolSchemaProjection.moonshot({
+        type: "object",
+        properties: {
+          linked: { $ref: "#/$defs/Linked", description: "drop me" },
+          tuple: { type: "array", items: [{ type: "string" }, { type: "number" }] },
+          prefixTuple: { type: "array", prefixItems: [{ type: "boolean" }, { type: "string" }] },
+        },
+      }),
+    ).toEqual({
+      type: "object",
+      properties: {
+        linked: { $ref: "#/$defs/Linked" },
+        tuple: { type: "array", items: { anyOf: [{ type: "string" }, { type: "number" }] } },
+        prefixTuple: { type: "array", items: { anyOf: [{ type: "boolean" }, { type: "string" }] } },
+      },
+    })
+  })
+
+  test("gemini handles numeric enums, dangling required fields, untyped arrays, and scalar object keys", () => {
+    expect(
+      ToolSchemaProjection.gemini({
+        type: "object",
+        required: ["status", "missing"],
+        properties: {
+          status: { type: "integer", enum: [1, 2] },
+          tags: { type: "array" },
+          name: { type: "string", properties: { ignored: { type: "string" } }, required: ["ignored"] },
+        },
+      }),
+    ).toEqual({
+      type: "object",
+      required: ["status"],
+      properties: {
+        status: { type: "string", enum: ["1", "2"] },
+        tags: { type: "array", items: { type: "string" } },
+        name: { type: "string" },
+      },
+    })
+  })
+
+  test("openai keeps one flat object top-level schema", () => {
+    expect(
+      ToolSchemaProjection.openAI({
+        anyOf: [
+          {
+            type: "object",
+            properties: {
+              path: { type: "string" },
+              maybe: { anyOf: [{ type: "string" }, { type: "null" }] },
+            },
+          },
+          { type: "object", properties: { resource: { type: "string" } } },
+        ],
+      }),
+    ).toEqual({
+      type: "object",
+      properties: {
+        path: { type: "string" },
+        maybe: { type: "string" },
+        resource: { type: "string" },
+      },
+      additionalProperties: false,
+    })
+  })
+
+  it.effect("applies model compatibility before protocol projection", () =>
+    Effect.gen(function* () {
+      const model = OpenAIChat.route
+        .with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
+        .model({ id: "kimi-k2", compatibility: { toolSchema: "moonshot" } })
+      const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
+        LLM.request({
+          model,
+          prompt: "Use the tool.",
+          tools: [
+            {
+              name: "lookup",
+              description: "Lookup data.",
+              inputSchema: {
+                type: "object",
+                anyOf: [
+                  {
+                    type: "object",
+                    properties: {
+                      tuple: { type: "array", items: [{ type: "string" }, { type: "number" }] },
+                      linked: { $ref: "#/$defs/Linked", description: "drop me" },
+                    },
+                  },
+                ],
+              },
+            },
+          ],
+        }),
+      )
+
+      expect(prepared.body.tools?.[0]?.function.parameters).toEqual({
+        type: "object",
+        properties: {
+          tuple: { type: "array", items: { anyOf: [{ type: "string" }, { type: "number" }] } },
+          linked: { $ref: "#/$defs/Linked" },
+        },
+        additionalProperties: false,
+      })
+    }),
+  )
+})