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Peekaboo/docs/spec.md
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Peekaboo: Full & Final Detailed Specification v1.1.1

https://aistudio.google.com/prompts/1B0Va41QEZz5ZMiGmLl2gDme8kQ-LQPW-

Project Vision: Peekaboo is a macOS utility exposed via a Node.js MCP server, enabling AI agents to perform advanced screen captures, image analysis via user-configured AI providers, and query application/window information. The core macOS interactions are handled by a native Swift command-line interface (CLI) named peekaboo, which is called by the Node.js server. All image captures automatically exclude window shadows/frames.

Core Components:

  1. Node.js/TypeScript MCP Server (peekaboo-mcp):
    • NPM Package Name: peekaboo-mcp.
    • GitHub Project Name: peekaboo.
    • Implements MCP server logic using the latest stable @modelcontextprotocol/sdk.
    • Exposes three primary MCP tools: image, analyze, list.
    • Translates MCP tool calls into commands for the Swift peekaboo CLI.
    • Parses structured JSON output from the Swift peekaboo CLI.
    • Handles image data preparation (reading files, Base64 encoding) for MCP responses if image data is explicitly requested by the client.
    • Manages interaction with configured AI providers based on environment variables. All AI provider calls (Ollama, OpenAI, etc.) are made from this Node.js layer.
    • Implements robust logging to a file using pino, ensuring no logs interfere with MCP stdio communication.
  2. Swift CLI (peekaboo):
    • A standalone macOS command-line tool, built as a universal binary (arm64 + x86_64).
    • Handles all direct macOS system interactions: image capture, application/window listing, and fuzzy application matching.
    • Does NOT directly interact with any AI providers (Ollama, OpenAI, etc.).
    • Outputs all results and errors in a structured JSON format via a global --json-output flag. This JSON includes a debug_logs array for internal Swift CLI logs, which the Node.js server can relay to its own logger.
    • The peekaboo binary is bundled at the root of the peekaboo-mcp NPM package.

I. Node.js/TypeScript MCP Server (peekaboo-mcp)

A. Project Setup & Distribution

  1. Language/Runtime: Node.js (latest LTS recommended, e.g., v18+ or v20+), TypeScript (latest stable, e.g., v5+).
  2. Package Manager: NPM.
  3. package.json:
    • name: "peekaboo-mcp"
    • version: Semantic versioning (e.g., 1.1.1).
    • type: "module" (for ES Modules).
    • main: "dist/index.js" (compiled server entry point).
    • bin: { "peekaboo-mcp": "dist/index.js" }.
    • files: ["dist/", "peekaboo"] (includes compiled JS and the Swift peekaboo binary at package root).
    • scripts:
      • build: Command to compile TypeScript (e.g., tsc).
      • start: node dist/index.js.
      • prepublishOnly: npm run build.
    • dependencies: @modelcontextprotocol/sdk (latest stable), zod (for input validation), pino (for logging), openai (for OpenAI API interaction). Support for other AI providers like Anthropic (e.g., using @anthropic-ai/sdk) is planned.
    • devDependencies: typescript, @types/node, pino-pretty (for optional development console logging).
  4. Distribution: Published to NPM. Installable via npm i -g peekaboo-mcp or usable with npx peekaboo-mcp.
  5. Swift CLI Location Strategy:
    • The Node.js server will first check the environment variable PEEKABOO_CLI_PATH. If set and points to a valid executable, that path will be used.
    • If PEEKABOO_CLI_PATH is not set or invalid, the server will fall back to a bundled path, resolved relative to its own script location (e.g., path.resolve(path.dirname(fileURLToPath(import.meta.url)), '..', 'peekaboo'), assuming the compiled server script is in dist/ and peekaboo binary is at the package root).

B. Server Initialization & Configuration (src/index.ts)

  1. Imports: McpServer, StdioServerTransport from @modelcontextprotocol/sdk; pino from pino; os, path from Node.js built-ins.
  2. Server Info: name: "PeekabooMCP", version: <package_version from package.json>.
  3. Server Capabilities: Advertise tools capability.
  4. Logging (Pino):
    • Instantiate pino logger.
    • Default Transport: File transport to path.join(os.tmpdir(), 'peekaboo-mcp.log'). Use mkdir: true option for destination.
    • Log Level: Controlled by ENV VAR PEEKABOO_LOG_LEVEL (standard Pino levels: trace, debug, info, warn, error, fatal). Default: "info".
    • Conditional Console Logging (Development Only): If ENV VAR PEEKABOO_MCP_CONSOLE_LOGGING="true", add a second Pino transport targeting process.stderr.fd (potentially using pino-pretty for human-readable output).
    • Strict Rule: All server operational logging must use the configured Pino instance. No direct console.log/warn/error that might output to stdout.
  5. Environment Variables (Read by Server):
    • PEEKABOO_AI_PROVIDERS: Comma-separated list of provider_name/default_model_for_provider pairs (e.g., "openai/gpt-4o,ollama/qwen2.5vl:7b"). Currently, recognized provider_name values are "openai" and "ollama". Support for "anthropic" is planned. If unset/empty, analyze tool reports AI not configured.
    • OPENAI_API_KEY: API key for OpenAI.
    • ANTHROPIC_API_KEY: API key for Anthropic (used for future planned support).
    • (Other cloud provider API keys as standard ENV VAR names).
    • PEEKABOO_OLLAMA_BASE_URL: Base URL for local Ollama instance. Default: "http://localhost:11434".
    • PEEKABOO_LOG_LEVEL: For Pino logger. Default: "info".
    • PEEKABOO_LOG_FILE: Path to the server's log file. Default: path.join(os.tmpdir(), 'peekaboo-mcp.log').
    • PEEKABOO_DEFAULT_SAVE_PATH: Default base absolute path for saving images captured by image if not specified in the tool input. If this ENV is also not set, the Swift CLI will use its own temporary directory logic.
    • PEEKABOO_CONSOLE_LOGGING: Boolean ("true"/"false") for dev console logs. Default: "false".
    • PEEKABOO_CLI_PATH: Optional override for Swift peekaboo CLI path.
  6. Server Status Reporting Logic:
    • A utility function generateServerStatusString() creates a formatted string: `"

--- Peekaboo MCP Server Status --- Name: PeekabooMCP Version: <server_version> Configured AI Providers (from PEEKABOO_AI_PROVIDERS ENV): <parsed list or 'None Configured. Set PEEKABOO_AI_PROVIDERS ENV.'> ---". * **Tool Descriptions:** When the server handles a ListToolsRequest(typically at client initialization), it appends thegenerateServerStatusString()output to thedescription field of each advertised tool (image, analyze, list). This provides clients with immediate server status information alongside tool capabilities. * **Direct Access via listtool:** The server status string can also be retrieved directly by calling thelisttool withitem_type: "server_status"(see Tool 3 details). 7. **Tool Registration:** Registerimage, analyze, listwith their Zod input schemas and handler functions. 8. **Transport:**await server.connect(new StdioServerTransport());. 9. **Shutdown:** Implement graceful shutdown on SIGINT, SIGTERM(e.g.,await server.close(); logger.flush(); process.exit(0);`).

C. MCP Tool Specifications & Node.js Handler Logic

General Node.js Handler Pattern (for tools calling Swift peekaboo CLI):

  1. Validate MCP input against the tool's Zod schema. If invalid, log error with Pino and return MCP error ToolResponse.
  2. Construct command-line arguments for Swift peekaboo CLI based on MCP input. Always include --json-output.
  3. Log the constructed Swift command with Pino at debug level.
  4. Execute Swift peekaboo CLI using child_process.spawn, capturing stdout, stderr, and exitCode.
  5. If any data is received on Swift CLI's stderr, log it immediately with Pino at warn level, prefixed (e.g., [SwiftCLI-stderr]).
  6. On Swift CLI process close:
    • If exitCode !== 0 or stdout is empty/not parseable as JSON:
      • Log failure details with Pino (error level).
      • Construct MCP error ToolResponse (e.g., errorCode: "SWIFT_CLI_EXECUTION_ERROR" or SWIFT_CLI_INVALID_OUTPUT in _meta).
      • Error Message Prioritization: The primary message in the error response will be derived by mapping the Swift CLI's exit code to a specific, user-friendly error message (e.g., "Screen Recording permission is not granted..."). If the exit code is unknown, the message will be derived from the Swift CLI's stderr output if available, prefixed with "Peekaboo CLI Error: ". Otherwise, a generic message like "Swift CLI execution failed (exit code: X)" will be used. The details field will contain stdout or stderr for additional context.
    • If exitCode === 0:
      • Attempt to parse stdout as JSON. If parsing fails, treat as error (above).
      • Let swiftResponse = JSON.parse(stdout).
      • If swiftResponse.debug_logs (array of strings) exists, log each entry via Pino at debug level, clearly marked as from backend (e.g., logger.debug({ backend: "swift", swift_log: entry })).
      • If swiftResponse.success === false:
        • Extract swiftResponse.error.message, swiftResponse.error.code, swiftResponse.error.details.
        • Construct and return MCP error ToolResponse, relaying these details (e.g., message in content, code in _meta.backend_error_code).
      • If swiftResponse.success === true:
        • Process swiftResponse.data to construct the success MCP ToolResponse.
        • Relay swiftResponse.messages as TextContentItems in the MCP response if appropriate.
        • For image with input.return_data: true:
          • Iterate swiftResponse.data.saved_files.[*].path.
          • For each path, read image file into a Buffer.
          • Base64 encode the Buffer.
          • Construct ImageContentItem for MCP ToolResponse.content, including data (Base64 string) and mimeType (from swiftResponse.data.saved_files.[*].mime_type).
    • Augment successful ToolResponse with initial server status string if applicable (see B.6).
    • Send MCP ToolResponse.

Tool 1: image

  • MCP Description: "Captures macOS screen content and optionally analyzes it. Targets can be the entire screen (each display separately), a specific application window, or all windows of an application, controlled by app_target. Supports foreground/background capture. Captured image(s) can be saved to a file (path), returned as Base64 data (format: \"data\"), or both. If a question is provided, the captured image is analyzed by an AI model chosen automatically from PEEKABOO_AI_PROVIDERS. Window shadows/frames are excluded."
  • MCP Input Schema (ImageInputSchema): 
    z.object({
  app_target: z.string().optional().describe(
    "Optional. Specifies the capture target. Examples:\\n" +
    "- Omitted/empty: All screens.\\n" +
    "- 'screen:INDEX': Specific display (e.g., 'screen:0').\\n" +
    "- 'frontmost': All windows of the current foreground app.\\n" +
    "- 'AppName': All windows of 'AppName'.\\n" +
    "- 'AppName:WINDOW_TITLE:Title': Window of 'AppName' with 'Title'.\\n" +
    "- 'AppName:WINDOW_INDEX:Index': Window of 'AppName' at 'Index'."
  ),
  path: z.string().optional().describe(
    "Optional. Base absolute path for saving the image. " +
    "If omitted and no `question` is asked, the tool returns the image as Base64 data without saving a persistent file. " +
    "If 'format' is 'data' and 'path' is also given, image is saved AND Base64 data is returned. " +
    "If 'question' is provided and 'path' is omitted, a temporary path is used for capture, and the file is deleted after analysis."
  ),
  question: z.string().optional().describe(
    "Optional. If provided, the captured image will be analyzed. " +
    "The server automatically selects an AI provider from 'PEEKABOO_AI_PROVIDERS'."
  ),
  format: z.enum(["png", "jpg", "data"]).optional().default("png").describe(
    "Output format. 'png' or 'jpg' save to 'path' (if provided). " +
    "'data' returns Base64 encoded PNG data inline; if 'path' is also given, saves a PNG file to 'path' too. " +
    "If 'path' is not given, 'format' defaults to 'data' behavior (inline PNG data returned)."
  ),
  capture_focus: z.enum(["background", "foreground"])
    .optional().default("background").describe(
      "Optional. Focus behavior. 'background' (default): capture without altering window focus. " +
      "'foreground': bring target to front before capture."
    )
})
    • Node.js Handler - app_target Parsing: The handler will parse app_target to determine the Swift CLI arguments for --app, --mode, --window-title, or --window-index.
      • Omitted/empty app_target: maps to Swift CLI --mode screen (no --app).
      • "screen:INDEX": maps to Swift CLI --mode screen --screen-index INDEX (custom Swift CLI flag might be needed or logic to select from multi-screen capture).
      • "frontmost": Node.js determines frontmost app (e.g., via list tool logic or new Swift CLI helper), then calls Swift CLI with that app and --mode multi (or window for main window).
      • "AppName": maps to Swift CLI --app AppName --mode multi.
      • "AppName:WINDOW_TITLE:Title": maps to Swift CLI --app AppName --mode window --window-title Title.
      • "AppName:WINDOW_INDEX:Index": maps to Swift CLI --app AppName --mode window --window-index Index.
    • Node.js Handler - format and path Logic:
      • If input.format === "data": return_data becomes effectively true. If input.path is also set, the image is saved to input.path (as PNG) AND Base64 PNG data is returned.
      • If input.format is "png" or "jpg":
        • If input.path is provided, the image is saved to input.path with the specified format. No Base64 data is returned unless input.question is also provided (for analysis).
        • If input.path is NOT provided: This implies format: "data" behavior; Base64 PNG data is returned.
      • If input.question is provided:
        • An effectivePath is determined (user's input.path or a temp path).
        • Image is captured to effectivePath.
        • Analysis proceeds as described below.
        • Base64 data is NOT returned in content due to analysis, but analysis_text is.
    • Node.js Handler - Analysis Logic (if input.question is provided):
      • An effectivePath is determined: if input.path is set, it's used. Otherwise, a temporary file path is generated.
      • Swift CLI is called to capture the image and save it to effectivePath.
      • The image file at effectivePath is read into a base64 string.
      • The AI provider and model are determined automatically by iterating through PEEKABOO_AI_PROVIDERS and selecting the first available/operational one (similar to analyze tool's "auto" mode, but without client provider_config override).
      • The image (base64) and input.question are sent to the chosen AI provider.
      • If a temporary path was used for capture (because input.path was omitted), the temporary image file is deleted after analysis.
      • The analysis_text and model_used are added to the tool's response.
      • Base64 image data (data field in ImageContentItem) is not included in the content array of the response when a question is asked.
    • Node.js Handler - Resilience with path and format: "data" (No question): If input.format === "data", input.question is NOT provided, and input.path is specified:
      • The handler will still attempt to process and return Base64 image data for successfully captured images even if the Swift CLI (or the handler itself) encounters an error saving to or reading from the user-specified input.path (or paths derived from it).
      • In such cases where image data is returned despite a save-to-path failure, a TextContentItem containing a "Peekaboo Warning:" message detailing the path saving issue will be included in the ToolResponse.content.
  • MCP Output Schema (ToolResponse):
    • content: Array<ImageContentItem | TextContentItem>
      • If input.format === "data" (or path was omitted, defaulting to "data" behavior) AND input.question is NOT provided: Contains one or more ImageContentItem(s): { type: "image", data: "<base64_png_string_no_prefix>", mimeType: "image/png", metadata?: { item_label?: string, window_title?: string, window_id?: number, source_path?: string } }.
      • If input.question IS provided, ImageContentItems with base64 image data are NOT added to content.
      • Always contains TextContentItem(s) (summary, file paths from saved_files if applicable and images were saved to persistent paths, Swift CLI messages, and analysis results if a question was asked).
    • saved_files: Array<{ path: string, item_label?: string, window_title?: string, window_id?: number, mime_type: string }>
      • Populated if input.path was provided (and not a temporary path for analysis that got deleted). The mime_type will reflect input.format if it was 'png' or 'jpg' and saved, or 'image/png' if format: "data" also saved a file.
      • If input.question is provided AND input.path was NOT specified (temp image used and deleted): This array will be empty.
    • analysis_text?: string: (Conditionally present if input.question was provided) Core AI answer or error/skip message.
    • model_used?: string: (Conditionally present if analysis was successful) e.g., "ollama/llava:7b", "openai/gpt-4o".
    • isError?: boolean (Can be true if capture fails, or if analysis is attempted but fails, even if capture succeeded).
    • _meta?: { backend_error_code?: string, analysis_error?: string } (For relaying Swift CLI error codes or analysis error messages).

Tool 2: analyze

  • MCP Description: "Analyzes an image file using a configured AI model (local Ollama, cloud OpenAI, etc.) and returns a textual analysis/answer. Requires image path. AI provider selection and model defaults are governed by the server's AI_PROVIDERS environment variable and client overrides."
  • MCP Input Schema (AnalyzeInputSchema): 
    z.object({
  image_path: z.string().describe("Required. Absolute path to image file (.png, .jpg, .webp) to be analyzed."),
  question: z.string().describe("Required. Question for the AI about the image."),
  provider_config: z.object({
    type: z.enum(["auto", "ollama", "openai" /* "anthropic" is planned */])
      .default("auto")
      .describe("AI provider. 'auto' uses server's PEEKABOO_AI_PROVIDERS ENV preference. Specific provider must be one of the currently implemented options ('ollama', 'openai') and enabled in server's PEEKABOO_AI_PROVIDERS."),
    model: z.string().optional().describe("Optional. Model name. If omitted, uses model from server's AI_PROVIDERS for chosen provider, or an internal default for that provider.")
  }).optional().describe("Optional. Explicit provider/model. Validated against server's PEEKABOO_AI_PROVIDERS.")
})
  • Node.js Handler Logic:
    1. Validate input. Server pre-checks image_path extension (.png, .jpg, .jpeg, .webp); return MCP error if not recognized.
    2. Read process.env.PEEKABOO_AI_PROVIDERS. If unset/empty, return MCP error "AI analysis not configured on this server. Set the PEEKABOO_AI_PROVIDERS environment variable." Log this with Pino (error level).
    3. Parse PEEKABOO_AI_PROVIDERS into configuredItems = [{provider: string, model: string}].
    4. Determine Provider & Model:
      • requestedProviderType = input.provider_config?.type || "auto".
      • requestedModelName = input.provider_config?.model.
      • chosenProvider: string | null = null, chosenModel: string | null = null.
      • If requestedProviderType !== "auto":
        • Find entry in configuredItems where provider === requestedProviderType.
        • If not found, MCP error: "Provider '{requestedProviderType}' is not enabled in server's PEEKABOO_AI_PROVIDERS configuration."
        • chosenProvider = requestedProviderType.
        • chosenModel = requestedModelName || model_from_matching_configuredItem || hardcoded_default_for_chosenProvider.
      • Else (requestedProviderType === "auto"):
        • Iterate configuredItems in order. For each {provider, modelFromEnv}:
          • Check availability (Ollama up? Cloud API key for provider set in process.env?).
          • If available: chosenProvider = provider, chosenModel = requestedModelName || modelFromEnv. Break.
        • If no provider found after iteration, MCP error: "No configured AI providers in PEEKABOO_AI_PROVIDERS are currently operational."
    5. Execute Analysis (Node.js handles all AI calls):
      • Read input.image_path into a Buffer. Base64 encode.
      • If chosenProvider is "ollama": Make direct HTTP POST calls to the Ollama API (e.g., /api/generate) using process.env.PEEKABOO_OLLAMA_BASE_URL. Handle Ollama API errors.
      • If chosenProvider is "openai": Use the official openai Node.js SDK with Base64 image, input.question, chosenModel, and API key from process.env.OPENAI_API_KEY. Handle OpenAI API errors.
      • If chosenProvider is "anthropic": (Currently not implemented) This would involve using the Anthropic SDK and API key from process.env.ANTHROPIC_API_KEY. For now, attempting to use Anthropic will result in an error.
    6. Construct MCP ToolResponse.
  • MCP Output Schema (ToolResponse):
    • content: [{ type: "text", text: "<AI's analysis/answer>" }, { type: "text", text: "👻 Peekaboo: Analyzed image with <provider>/<model> in X.XXs." }] (The second text item provides feedback on the analysis process).
    • analysis_text: string (Core AI answer).
    • model_used: string (e.g., "ollama/llava:7b", "openai/gpt-4o") - The actual provider/model pair used.
    • isError?: boolean
    • _meta?: { backend_error_code?: string } (For AI provider API errors).

Tool 3: list

  • MCP Description: "Lists system items: running applications with one or more windows, all windows of a specific app, or server status. App ID uses fuzzy matching."
  • MCP Input Schema (ListInputSchema): 
    z.object({
  item_type: z.enum(["running_applications", "application_windows", "server_status"])
    .default("running_applications").describe("What to list. 'server_status' returns Peekaboo server info."),
  app: z.string().optional().describe("Required when 'item_type' is 'application_windows'. Specifies the target application by name (e.g., 'Safari') or bundle ID. Fuzzy matching is used."),
  include_window_details: z.array(
    z.enum(["ids", "bounds", "off_screen"])
  ).optional().describe("Optional, for 'application_windows' only. Specifies additional details for each window. If provided for other 'item_type' values, it will be ignored only if it is an empty array.")
}).refine(data => data.item_type !== "application_windows" || (data.app !== undefined && data.app.trim() !== ""), {
  message: "'app' identifier is required when 'item_type' is 'application_windows'.", path: ["app"],
}).refine(data => !data.include_window_details || data.include_window_details.length === 0 || data.item_type === "application_windows", {
  message: "'include_window_details' is only applicable when 'item_type' is 'application_windows'.",
  path: ["include_window_details"]
})
  • Node.js Handler Logic:
    • If input.item_type === "server_status": Handler directly calls generateServerStatusString() (after retrieving the server version) and returns the resulting string in ToolResponse.content[{type:"text"}]. Does NOT call Swift CLI.
    • Else (for "running_applications", "application_windows"): Call Swift peekaboo list ... with mapped args (including joining include_window_details array to comma-separated string for Swift CLI flag). Parse Swift JSON. Format MCP ToolResponse.
  • MCP Output Schema (ToolResponse):
    • content: [{ type: "text", text: "<Summary or Status String>" }]
    • If item_type: "running_applications": application_list: Array<{ app_name: string; bundle_id: string; pid: number; is_active: boolean; window_count: number }>.
    • If item_type: "application_windows":
      • window_list: Array<{ window_title: string; window_id?: number; window_index?: number; bounds?: {x:number,y:number,w:number,h:number}; is_on_screen?: boolean }>.
      • target_application_info: { app_name: string; bundle_id?: string; pid: number }.
    • isError?: boolean
    • _meta?: { backend_error_code?: string }

II. Swift CLI (peekaboo)

A. General CLI Design

  1. Executable Name: peekaboo (Universal macOS binary: arm64 + x86_64).
  2. Argument Parser: Use swift-argument-parser package.
  3. Top-Level Commands (Subcommands of peekaboo): image, list. (No analyze command).
  4. Global Option (for all commands/subcommands): --json-output (Boolean flag).
    • If present: All stdout from Swift CLI MUST be a single, valid JSON object. stderr should be empty on success, or may contain system-level error text on catastrophic failure before JSON can be formed.
    • If absent: Output human-readable text to stdout and stderr as appropriate for direct CLI usage.
    • Success JSON Structure: 
      {
  "success": true,
  "data": { /* Command-specific structured data */ },
  "messages": ["Optional user-facing status/warning message from Swift CLI operations"],
  "debug_logs": ["Internal Swift CLI debug log entry 1", "Another trace message"]
}
    • Error JSON Structure: 
      {
  "success": false,
  "error": {
    "message": "Detailed, user-understandable error message.",
    "code": "SWIFT_ERROR_CODE_STRING", // e.g., PERMISSION_DENIED_SCREEN_RECORDING
    "details": "Optional additional technical details or context."
  },
  "debug_logs": ["Contextual debug log leading to error"]
}
    • Standardized Swift Error Codes (error.code values):
      • PERMISSION_ERROR_SCREEN_RECORDING
      • PERMISSION_ERROR_ACCESSIBILITY
      • APP_NOT_FOUND
      • AMBIGUOUS_APP_IDENTIFIER
      • WINDOW_NOT_FOUND
      • CAPTURE_FAILED
      • FILE_IO_ERROR
      • INVALID_ARGUMENT
      • SIPS_ERROR
      • INTERNAL_SWIFT_ERROR
      • UNKNOWN_ERROR
  5. Permissions Handling:
    • The CLI must proactively check for Screen Recording permission before attempting any capture or window listing that requires it (e.g., reading window titles via CGWindowListCopyWindowInfo).
    • If Accessibility is used for --capture-focus foreground window raising, check that permission.
    • If permissions are missing, output the specific JSON error (e.g., code PERMISSION_ERROR_SCREEN_RECORDING) and exit with a distinct exit code for that error. Do not hang or prompt interactively.
  6. Temporary File Management:
    • If the CLI needs to save an image temporarily (e.g., if screencapture is used as a fallback for PDF, or if no --path is given by Node.js), it uses FileManager.default.temporaryDirectory with unique filenames (e.g., peekaboo_<uuid>_<info>.<format>).
    • These self-created temporary files MUST be deleted by the Swift CLI after it has successfully generated and flushed its JSON output to stdout.
    • Files saved to a user/Node.js-specified --path are NEVER deleted by the Swift CLI.
  7. Internal Logging for --json-output:
    • When --json-output is active, internal verbose/debug messages are collected into the debug_logs: [String] array in the final JSON output. They are NOT printed to stderr.
    • For standalone CLI use (no --json-output), these debug messages can print to stderr.

B. peekaboo image Command

  • Options (defined using swift-argument-parser):
    • --app <String?>: App identifier.
    • --path <String?>: Base output directory or file prefix/path.
    • --mode <ModeEnum?>: ModeEnum is screen, window, multi. Default logic: if --app then window, else screen.
    • --window-title <String?>: For mode window.
    • --window-index <Int?>: For mode window.
    • --format <FormatEnum?>: FormatEnum is png, jpg. Default png.
    • --capture-focus <FocusEnum?>: FocusEnum is background, foreground. Default background.
  • Behavior:
    • Implements fuzzy app matching. On ambiguity, returns JSON error with code: "AMBIGUOUS_APP_IDENTIFIER" and lists potential matches in error.details or error.message.
    • Always attempts to exclude window shadow/frame (CGWindowImageOption.boundsIgnoreFraming or screencapture -o if shelled out for PDF). No cursor is captured.
    • Background Capture (--capture-focus background or default):
      • Primary method: Uses CGWindowListCopyWindowInfo to identify target window(s)/screen(s).
      • Captures via CGDisplayCreateImage (for screen mode) or CGWindowListCreateImageFromArray (for window/multi modes).
      • Converts CGImage to Data (PNG or JPG) and saves to file (at user --path or its own temp path).
    • Foreground Capture (--capture-focus foreground):
      • Activates app using NSRunningApplication.activate(options: [.activateIgnoringOtherApps]).
      • If a specific window needs raising (e.g., from --window-index or specific --window-title for an app with many windows), it may attempt to use Accessibility API (AXUIElementPerformAction(kAXRaiseAction)) if available and permissioned.
      • If specific window raise fails (or Accessibility not used/permitted), it logs a warning to the debug_logs array (e.g., "Could not raise specific window; proceeding with frontmost of activated app.") and captures the most suitable front window of the activated app.
      • Capture mechanism is still preferably native CG APIs.
    • Multi-Screen (--mode screen): Enumerates CGGetActiveDisplayList, captures each display using CGDisplayCreateImage. Filenames (if saving) get display-specific suffixes (e.g., _display0_main.png, _display1.png).
    • Multi-Window (--mode multi): Uses CGWindowListCopyWindowInfo for target app's PID, captures each relevant window (on-screen by default) with CGWindowListCreateImageFromArray. Filenames get window-specific suffixes.
    • PDF Format Handling (as per Q7 decision): If --format pdf were still supported (it's removed), it would use Process to call screencapture -t pdf -R<bounds> or -l<id>. Since PDF is removed, this is not applicable.
  • JSON Output data field structure (on success): 
    {
  "saved_files": [ // Array is always present, even if empty (e.g. capture failed before saving)
    {
      "path": "/absolute/path/to/saved/image.png", // Absolute path
      "item_label": "Display 1 / Main", // Or window_title for window/multi modes
      "window_id": 12345, // CGWindowID (UInt32), optional, if available & relevant
      "window_index": 0,  // Optional, if relevant (e.g. for multi-window or indexed capture)
      "mime_type": "image/png" // Actual MIME type of the saved file
    }
    // ... more items if mode is screen or multi ...
  ]
}

C. peekaboo list Command

  • Subcommands & Options:
    • peekaboo list apps [--json-output]
    • peekaboo list windows --app <app_identifier_string> [--include-details <comma_separated_string_of_options>] [--json-output]
      • --include-details options: off_screen, bounds, ids.
  • Behavior:
    • apps: Uses NSWorkspace.shared.runningApplications. For each app, retrieves localizedName, bundleIdentifier, processIdentifier (pid), isActive. To get window_count, it performs a CGWindowListCopyWindowInfo call filtered by the app's PID and counts on-screen windows.
    • windows:
      • Resolves app_identifier using fuzzy matching. If ambiguous, returns JSON error.
      • Uses CGWindowListCopyWindowInfo filtered by the target app's PID.
      • If --include-details contains "off_screen", uses CGWindowListOption.optionAllScreenWindows (and includes kCGWindowIsOnscreen boolean in output). Otherwise, uses CGWindowListOption.optionOnScreenOnly.
      • Extracts kCGWindowName (title).
      • If "ids" in --include-details, extracts kCGWindowNumber as window_id.
      • If "bounds" in --include-details, extracts kCGWindowBounds as bounds: {x, y, width, height}.
      • window_index is the 0-based index from the filtered array returned by CGWindowListCopyWindowInfo (reflecting z-order for on-screen windows).
  • JSON Output data field structure (on success):
    • For apps: 
      {
  "applications": [
    {
      "app_name": "Safari",
      "bundle_id": "com.apple.Safari",
      "pid": 501,
      "is_active": true,
      "window_count": 3 // Count of on-screen windows for this app
    }
    // ... more applications ...
  ]
}
    • For windows: 
      {
  "target_application_info": {
    "app_name": "Safari",
    "pid": 501,
    "bundle_id": "com.apple.Safari"
  },
  "windows": [
    {
      "window_title": "Apple",
      "window_id": 67, // if "ids" requested
      "window_index": 0,
      "is_on_screen": true, // Potentially useful, especially if "off_screen" included
      "bounds": {"x": 0, "y": 0, "width": 800, "height": 600} // if "bounds" requested
    }
    // ... more windows ...
  ]
}

III. Build, Packaging & Distribution

  1. Swift CLI (peekaboo):
    • Package.swift defines an executable product named peekaboo.
    • Build process (e.g., part of NPM prepublishOnly or a separate build script): swift build -c release --arch arm64 --arch x86_64.
    • The resulting universal binary (e.g., from .build/apple/Products/Release/peekaboo) is copied to the root of the peekaboo-mcp NPM package directory before publishing.
  2. Node.js MCP Server:
    • TypeScript is compiled to JavaScript (e.g., into dist/) using tsc.
    • The NPM package includes dist/ and the peekaboo Swift binary (at package root).

IV. Documentation (README.md for peekaboo-mcp NPM Package)

  1. Project Overview: Briefly state vision and components.
  2. Prerequisites:
    • macOS version (e.g., 12.0+ or as required by Swift/APIs).
    • Xcode Command Line Tools (recommended for a stable development environment on macOS, even if not strictly used by the final Swift binary for all operations).
    • Ollama (if using local Ollama for analysis) + instructions to pull models.
  3. Installation:
    • Primary: npm install -g peekaboo-mcp.
    • Alternative: npx peekaboo-mcp.
  4. MCP Client Configuration:
    • Provide example JSON snippets for configuring popular MCP clients (e.g., VS Code, Cursor) to use peekaboo-mcp.
    • Example for VS Code/Cursor using npx for robustness: 
      {
  "mcpServers": {
    "PeekabooMCP": {
      "command": "npx",
      "args": ["peekaboo-mcp"],
      "env": {
        "PEEKABOO_AI_PROVIDERS": "ollama/llava:latest,openai/gpt-4o",
        "OPENAI_API_KEY": "sk-yourkeyhere"
        /* other ENV VARS */
      }
    }
  }
}
  5. Required macOS Permissions:
    • Screen Recording: Essential for ALL image functionalities and for list if it needs to read window titles (which it does via CGWindowListCopyWindowInfo). Provide clear, step-by-step instructions for System Settings. Include open "x-apple.systempreferences:com.apple.preference.security?Privacy_ScreenCapture" command.
    • Accessibility: Required only if image with capture_focus: "foreground" needs to perform specific window raising actions (beyond simple app activation) via the Accessibility API. Explain this nuance. Include open "x-apple.systempreferences:com.apple.preference.security?Privacy_Accessibility" command.
  6. Environment Variables (for Node.js peekaboo-mcp server):
    • PEEKABOO_AI_PROVIDERS: Crucial for analyze. Explain format (provider/model,provider/model), effect, and that analyze reports "not configured" if unset. List recognized provider names ("ollama", "openai").
    • OPENAI_API_KEY (and similar for other cloud providers): How they are used.
    • PEEKABOO_OLLAMA_BASE_URL: Default and purpose.
    • PEEKABOO_LOG_LEVEL: For pino logger. Values and default.
    • PEEKABOO_LOG_FILE: Path to the server's log file. Default: path.join(os.tmpdir(), 'peekaboo-mcp.log').
    • PEEKABOO_DEFAULT_SAVE_PATH: Default base absolute path for saving images captured by image if not specified in the tool input. If this ENV is also not set, the Swift CLI will use its own temporary directory logic.
    • PEEKABOO_CONSOLE_LOGGING: For development.
    • PEEKABOO_CLI_PATH: For overriding bundled Swift CLI.
  7. MCP Tool Overview:
    • Brief descriptions of image, analyze, list and their primary purpose.
  8. Link to Detailed Tool Specification: A separate TOOL_API_REFERENCE.md (generated from or summarizing the Zod schemas and output structures in this document) for users/AI developers needing full schema details.
  9. Troubleshooting / Support: Link to GitHub issues.

V. Testing Strategy

Comprehensive testing is crucial for ensuring the reliability and correctness of Peekaboo. The strategy includes unit tests for individual modules, integration tests for component interactions, and end-to-end tests for validating complete user flows.

A. Unit Tests

  • Node.js Server (src/): Unit tests are written using Jest for utility functions, individual tool handlers (mocking Swift CLI execution and AI provider calls), and schema validation logic. Focus is on isolating and testing specific pieces of logic.
  • Swift CLI (peekaboo-cli/): Swift XCTests are used to test individual functions, argument parsing, JSON serialization/deserialization, and core macOS interaction logic (potentially mocking system calls where feasible or testing against known system states).

B. Integration Tests

  • Node.js Server & Swift CLI: Tests that verify the correct interaction between the Node.js server and the Swift CLI. This involves the Node.js server actually spawning the Swift CLI process and validating that arguments are passed correctly and JSON responses are parsed as expected. These tests might use a real (but controlled) Swift CLI binary.
  • Node.js Server & AI Providers: Tests that verify the interaction with AI providers. These would typically involve mocking the AI provider SDKs/APIs to simulate various responses (success, error, specific content) and ensure the Node.js server handles them correctly.

C. End-to-End (E2E) Tests

E2E tests validate the entire system flow from the perspective of an MCP client. They ensure all components work together as expected.

  1. Setup:

    • The test runner will start an instance of the peekaboo-mcp server.
    • The environment will be configured appropriately (e.g., PEEKABOO_AI_PROVIDERS pointing to mock services or controlled real services, PEEKABOO_LOG_LEVEL set for test visibility).
    • A mock Swift CLI could be used for some scenarios to control its output precisely, or the real Swift CLI for full integration.

  2. Test Scenarios (Examples):

    • Tool Discovery: Client sends ListToolsRequest, verifies the correct tools (image, analyze, list) and their schemas are returned.
    • image tool - Screen Capture:
      • Call image to capture the entire screen and save to a file. Verify the file is created and is a valid image.
      • Call image to capture a specific (test) application's window, save to file, and return data. Verify file creation, image data in response, and correct metadata.
      • Test different modes (screen, window, multi) and options (format, capture_focus).
      • Test error conditions: invalid app name, permissions not granted (if testable in CI environment or via mocks).
    • analyze tool - Image Analysis:
      • Provide a test image and a question. Configure PEEKABOO_AI_PROVIDERS to use a mock AI service.
      • Call analyze, verify the mock AI service was called with the correct parameters (image data, question, model).
      • Verify the mock AI service's response is correctly relayed in the MCP ToolResponse.
      • Test with different AI provider configurations (auto, specific). Test error handling if AI provider is unavailable or returns an error.
    • list tool - Listing System Items:
      • Call list for running_applications. Verify the structure of the response (may need to mock Swift CLI or run in a controlled environment to get predictable app lists).
      • Call list for application_windows of a known (test) application. Verify window details.
      • Call list for server_status. Verify the server status string is returned.
      • Test error conditions: app not found for application_windows.

  3. Tooling:

    • E2E tests can be written using a test runner like Jest, combined with a library or custom code to simulate an MCP client (i.e., send JSON-RPC requests and receive responses over stdio if testing against a server started with StdioServerTransport).
    • Assertions will be made on the MCP ToolResponse objects and any side effects (e.g., files created, logs written).

  4. Execution:

    • E2E tests are typically run as a separate suite, often in a CI/CD pipeline, as they can be slower and require more setup than unit or integration tests.