Adding an AI Chat Tool

ryOS chat tools follow the Vercel AI SDK tool-loop pattern. A tool is defined on the server (name + description + Zod inputSchema), and is either:

• Server-executed — has an execute fn that runs in api/chat/tools/ (needs Redis, secrets, server fetch). Add the name to SERVEREXECUTEDTOOL_NAMES.
• Client-executed — has no execute. The model emits a tool call, the browser runs a handler in src/apps/chats/tools/, and the result is sent back via addToolOutput (needs Zustand stores, IndexedDB, media/DOM APIs).

File Map

Decision: Server or Client?

Some tools run both ways (e.g. stickiesControl, calendarControl, contactsControl): client in web chat, server in the Telegram profile. In that case provide both a handler and an executor.

A. Add the Schema (api/chat/tools/schemas.ts)

Schemas are Zod. Keep action-style tools as a discriminated/enum action plus optional params, and use .superRefine for cross-field rules so the model gets a clear error before the handler runs.

export const myFeatureControlSchema = z.object({
  action: z.enum(["list", "create", "delete"]),
  id: z.string().optional(),
  content: z.string().optional(),
});

Add any shared enums/constants to types.ts and import them here (mirrors CALENDARACTIONS, TVACTIONS, etc.).

B. Define the Tool (api/chat/tools/index.ts)

1. Add a clear, behavior-specifying entry to TOOL_DESCRIPTIONS (the model relies heavily on this — describe each action, required params, and when NOT to use it).
2. Add the tool to the allTools object inside createChatTools.

Client-executed (no execute):

myFeatureControl: {
  description: TOOL_DESCRIPTIONS.myFeatureControl,
  inputSchema: schemas.myFeatureControlSchema,
  // No execute — handled client-side (requires Zustand store access)
},

Server-executed:

myFeatureControl: {
  description: TOOL_DESCRIPTIONS.myFeatureControl,
  inputSchema: schemas.myFeatureControlSchema,
  execute: async (input: MyFeatureControlInput) => executeMyFeatureControl(input, context),
},

If the tool should be available to the Telegram/memory profiles, also add it to the relevant branch in createChatTools (the telegram profile object or MEMORYTOOLNAMES). Tools default to the "all" profile.

C. Mark Execution Side (src/shared/tools/serverExecuted.ts)

If (and only if) the tool is server-executed, add it to TOOLEXECUTIONMETADATA with execution: "server". The client uses SERVEREXECUTEDTOOLNAMESET to skip client dispatch for these (it returns early without running a handler).

export const TOOL_EXECUTION_METADATA = [
  // ...
  { name: "myFeatureControl", execution: "server" },
] as const;

Client-executed tools do NOT go here.

D. Client Handler (client-executed tools only)

Create src/apps/chats/tools/myFeatureHandler.ts. The handler reads/writes Zustand stores and reports a result through context.addToolOutput.

import type { ToolContext } from "./types";
import { useMyFeatureStore } from "@/stores/useMyFeatureStore";
import { useAppStore } from "@/stores/useAppStore";
import i18n from "@/lib/i18n";

export interface MyFeatureControlInput {
  action: "list" | "create" | "delete";
  id?: string;
  content?: string;
}

export const handleMyFeatureControl = (
  input: MyFeatureControlInput,
  toolCallId: string,
  context: ToolContext
): void => {
  const store = useMyFeatureStore.getState();
  try {
    switch (input.action) {
      case "list": {
        context.addToolOutput({
          tool: "myFeatureControl",
          toolCallId,
          output: JSON.stringify(store.items, null, 2),
        });
        break;
      }
      // create / delete ...
      default:
        context.addToolOutput({
          tool: "myFeatureControl",
          toolCallId,
          state: "output-error",
          errorText: i18n.t("apps.chats.toolCalls.unknownError"),
        });
    }
  } catch (error) {
    context.addToolOutput({
      tool: "myFeatureControl",
      toolCallId,
      state: "output-error",
      errorText: error instanceof Error ? error.message : "error",
    });
  }
};

Handler conventions (match existing handlers like stickiesHandler.ts):

• Always emit exactly one addToolOutput per call — a success output string or an { state: "output-error", errorText }.
• Localize user-facing strings via i18n.t(...) (apps.chats.toolCalls.*).
• Open the relevant app first when a mutation should surface it (context.launchApp("myfeature") / guard with useAppStore.getState().getInstancesByAppId).
• For list→mutate flows where the AI passes IDs back, use createShortIdMap / resolveId from ./helpers to keep token usage low.

ToolContext provides { launchApp, addToolOutput, detectUserOS }.

E. Wire the Client Dispatch

1. In src/apps/chats/tools/index.ts, export the handler and its input type.
2. In src/apps/chats/tools/dispatchToolCall.ts (shared by the Chats app and the desktop assistant), add a case to the switch (toolCall.toolName):

case "myFeatureControl": {
  handleMyFeatureControl(
    toolCall.input as MyFeatureControlInput,
    toolCall.toolCallId,
    toolContext
  );
  result = ""; // handler already called addToolOutput
  break;
}

Dispatch is an explicit switch — there is no handler registry. Add the case, otherwise the tool falls through to the default branch and reports "Unhandled tool". Set result = "" when the handler emits its own output (return a non-empty string only for trivial tools that don't call addToolOutput). VFS tools (list/open/read/write/edit) live in vfsHandlers.ts and receive a VfsToolContext with saveFile + recordOpenedInstance.

F. Server Executor (server-executed / dual tools)

Add executeMyFeatureControl(input, context) to executors.ts (or app-state-executors.ts for app-state tools), export it from api/chat/tools/index.ts, and reference it in the tool's execute. The executor receives the server context (MemoryToolContext: logging, env, redis/auth helpers) and must return a JSON-serializable result.

For tools that return images to the model (like infiniteMacControl's readScreen), add a toModelOutput that converts the result into multimodal content.

Testing

• Schema (fast, no server): add a tests/unit/<domain>/test-<feature>-schema.test.ts that safeParses valid and invalid inputs (see tests/unit/media/test-media-control-unified.test.ts). Unit suites are auto-discovered. See the write-tests skill.
• Server executor: cover via the AI endpoint suite (test:ai) where applicable.
• Client handler / end-to-end: exercise in the Chats app by asking Ryo to use the capability and confirming the store/app updates and the tool result bubble.

Checklist

- [ ] Schema in api/chat/tools/schemas.ts (+ shared enums in types.ts)
- [ ] Description in TOOL_DESCRIPTIONS + entry in createChatTools (right profile)
- [ ] If server-executed: add to TOOL_EXECUTION_METADATA (serverExecuted.ts) + write executor
- [ ] If client-executed: handler in src/apps/chats/tools/ + export + switch case in useAiChat.ts
- [ ] Localize tool-call strings (apps.chats.toolCalls.*)
- [ ] Schema unit test in tests/ (registered in test:unit)