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Raspberry Pi 5📖
00.Quick Start (Must See)
  • 1.Quick Start (Must See)
  • 2.TF card expand capacity
  • 3.Raspberry Pi 5 - 1GB Version Must - See Before Use
01.Introduction to Raspberry PI 5
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Installation and introduction of shell and active radiator
  • 6. GPIO and 40-pin pins
  • 7. Tips for using Raspberry PI 5
  • 8. Raspberry PI PCIe connector
  • 9. MIPI CSI DSI connector
  • 10. UART connector
  • 11. Real-time clock
  • 12. Power over Ethernet (PoE) connector
02.Introduction to common mirror systems
  • 1. Official Raspberry PI system
  • 2. Ubuntu
03.Basic settings of Raspberry Pi
  • 1. Update and upgrade operating system
  • 2.Introduction to raspi-config tool
  • 3.config.txt file description
  • 4.Network Configuration
  • 5.remote access
  • 6.Transfer files remotely
  • 7.Set display resolution and rotation
  • 8.Set screen to sleep
  • 9.Get real-time temperature of Raspberry Pi
  • 10.Play audio and video
  • 11.Using USB camera
  • 12.Using MIPI camera
04.Basic programming course
1. C language programming
  • 1. C language environment construction
  • 2. Print Hello World
2. Python programming
  • 1.Python usage
  • 2.Use APT to install Python packages
  • 3.Python virtual environment
  • 4.GPIO Zero
  • 5.Pin output high and low levels
  • 6.Pin reads high and low levels
  • 7.Pin output PWM
3.Jupyter lab programming
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Lab
  • 3.Jupyter Lab plug-in installation
  • 4.Basic use of Jupyter Widgets
05.Advanced AI course
5.Advanced AI gameplay
  • 1. Human pose estimation
  • 2. target detection
  • 3.yolov5
  • 4. Camera display
  • 5. QR code
  • 6. face detection
  • 7. Color recognition
2.Getting started with Open Source CV
  • 1. Introduction to Open Source CV
  • 2. Read and display Open Source CV images
  • 3. Open Source CV image writing
  • 4. Open Source CV image quality
  • 5.Open Source CV pixel operation
3.Open Source CV Geometric Transformations
  • 1. Image resizing
  • 2. Cut the image
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine transformation
  • 6. Rotate the image
  • 7. Perspective transformation
4.Text Picture Drawing Open Source CV image processing and drawing text line segments
  • 1、Grey Processing
  • 2、image binaryzation
  • 3、edge detection
  • 4、Line segment drawing
  • 5、Rectangle and circle drawing
  • 6、Text image drawing
1.Open Source CV environment setup
  • Open Source CV environment setup
6.YOLOv11 development
  • 1. YOLOv11 environment construction
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model conversion
  • 9. Dataset Annotation
  • 10. Model training_conversion
  • 11. Model prediction
7.YOLO26 development
  • 01.YOLO26 environment
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented bounding box object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training - conversion
  • 11.Model prediction
06.Mediapipe course
  • 1. Face special effects
  • 2. Face detection
  • 3. Three-dimensional object recognition
  • 4. Paintbrush
  • 5. Finger control
  • 6. Gesture recognition
07.Docker Tutorial
  • 1.Overview and installation
  • 2.Common commands
  • 3.Understand and publish images
  • 4.Hardware interaction data processing
  • 5.Enter docker container
  • 6.Update docker images
  • 7. docker container to install ROS using accessories
08.ROS1 advanced tutorial
1.ROS1 basic course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
2.ROS1 vision application
  • 0.Precautions
  • 1.AR vision
  • 2.AR QR code
  • 3.ROS+Opencv basics
  • 4.Face recognition
  • 5.harris corner detection
  • 6.Target tracking algorithm
  • 7.Contour moment
  • 8.Polygon outline
  • 9.Discrete fourier transform algorithm
  • 10.Edge detection algorithm
  • 11.Face detection algorithm
  • 12.Optical flow detection algorithm
  • 13.Contour detection
  • 14.General contour detection
  • 15.Feature point tracking
  • 16.HLS color filtering
  • 17.Hough circle detection
  • 18.Hough linear detection
  • 19.HSV color filtering
  • 20.LK optical flow algorithm
  • 21.detection algorithm
  • 22.Phase dependent displacement detection
  • 23.Image pyramid sampling algorithm
  • 24.RGB color filtering
  • 25.Clear background detection algorithm
  • 26.Simplified optical flow algorithm
  • 27.Simple filter
  • 28.Threshold image processing
  • 29.Watershed segmentation algorithm
09.ROS2 advanced tutorial
1.ROS2 basic course
  • 1. Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
2.ROS2 vision application
  • 0.Precautions
  • 1.Opencv basic course
  • 2.ROS+opencv application
  • 3.QR code creation and recognition
  • 4.AR vision
  • 5.Interesting gameplay of mediapipe
10.Offline AI model coursenew
Offline large model (Text Interaction)
  • 0. AI large model image description
  • 1. Deployment of large AI model environments
  • 2. Switch Chinese input method
  • 3. Installation of large model dialogue platform
  • 4. Meta AI Llama 3.2 model
  • 5. Alibaba Cloud Qwen3 Model
  • 6. Microsoft Phi-4-mini model
  • 7. DeepSeek DeepSeek-R1 model
  • 8. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 9. Google Gemma3 Visual Multimodal Large Model
  • 10. Llava Visual Multimodal Large Model
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Visual Understanding Applications
  • 13. Multimodal Text-Based Image Application
  • 14. Multimodal Video Analysis Application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal table scanning application
  • 17. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
11.Online AI model coursenew
Online large model (text interaction)
  • 1.OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal Visual Understanding Applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (voice interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
12.OpenClaw Deployment and Basic Usagenew
  • 01. OpenClaw Deployment
  • 02. OpenClaw WhatsApp Plugin Application
  • 03. OpenClaw WebChat Interaction
  • 04. OpenClaw TUI Interaction
  • 05. OpenClaw Tools Introduction
  • 06. OpenClawGate Gateway User Manual
  • 07. OpenClaw Features Overview
  • 08. OpenClawHub Introduction (Skill Installation)
  • 09. OpenClaw Application - File Management
  • 10. OpenClaw Application - Camera
  • 11. OpenClaw Application - Browser Control
  • 12. OpenClaw Application - Script Execution
  • 13. OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 14. OpenClaw Application - Dedicated AI Assistant
13.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 01. Peripheral Hardware Configuration
  • 02. OpenClaw API-KEY Configuration
  • 03. OpenClaw Model Switching
  • 04. OpenClaw Prompts
  • 05. AI Voice Interaction Configuration
  • 06. 3D Configuration Tests
2.OpenClaw AI Programming Peripheral Control
  • 1. Servo Control
  • 2. RGB Strip
  • 3. OLED Display
  • 4. Temperature and Humidity Sensor
  • 5. Camera Application
  • 6. AI Script Peripheral Control
3.OpenClaw Peripheral Integration Applications
  • 1. Plant Care Assistant
  • 2. AI Calorie Estimation
  • 3. AI Rock Paper Scissors Game
  • 4. AI Pet
  • 5. AI Weather Station
  • 6. Temperature and Humidity Dashboard
  • 7. Scheduled Tasks
Download
  • SystemFile
  • Code
  • SoftWare
  • 3D_Model_File
  • GuideManual
Download ZIP

Welcome to Raspberry Pi 5 repository

05. OpenClaw Tools Introduction

Tools

OpenClaw exposes first-class agent tools for browser, canvas, nodes, and cron. These tools replace the old openclaw-* Skills: tools are typed, don't require shell calls, and agents should depend on them directly.

Disabling Tools

You can globally allow/deny tools via tools.allow / tools.deny in openclaw.json (deny takes precedence). This blocks disallowed tools from being sent to the model provider.

{
  tools: { deny: ["browser"] },
}

Note:

  • Matching is case-insensitive.
  • Supports * wildcard ("*" for all tools).
  • If tools.allow only references unknown or unloaded plugin tool names, OpenClaw will log a warning and ignore the allow list to ensure core tools remain available.

Tool Profiles (Base Allow Lists)

tools.profile sets a base tool allow list before tools.allow/tools.deny. Overridable per agent: agents.list[].tools.profile. Profiles:

  • minimal: Only session_status
  • coding: group:fs, group:runtime, group:sessions, group:memory, image
  • messaging: group:messaging, sessions_list, sessions_history, sessions_send, session_status
  • full: Unrestricted (same as unset)

Example (messaging by default, also allow Slack + Discord tools):

xxxxxxxxxx
{
  tools: {
    profile: "messaging",
    allow: ["slack", "discord"],
  },
}

Example (coding profile, but deny exec/process everywhere):

xxxxxxxxxx
{
  tools: {
    profile: "coding",
    deny: ["group:runtime"],
  },
}

Example (global coding profile, messaging-only support agent):

xxxxxxxxxx
{
  tools: { profile: "coding" },
  agents: {
    list: [
      {
        id: "support",
        tools: { profile: "messaging", allow: ["slack"] },
      },
    ],
  },
}

Provider-Specific Tool Policies

Use tools.byProvider to further restrict tools for specific providers (or individual provider/model) without changing your global defaults. Overridable per agent: agents.list[].tools.byProvider. This applies after base tool profiles and before allow/deny lists, so it can only narrow the tool set. Provider keys accept provider (e.g., google-antigravity) or provider/model (e.g., openai/gpt-5.2). Example (keep global coding profile, but Google Antigravity uses minimal tools):

xxxxxxxxxx
{
  tools: {
    profile: "coding",
    byProvider: {
      "google-antigravity": { profile: "minimal" },
    },
  },
}

Example (provider/model-specific allow list for unstable endpoints):

xxxxxxxxxx
{
  tools: {
    allow: ["group:fs", "group:runtime", "sessions_list"],
    byProvider: {
      "openai/gpt-5.2": { allow: ["group:fs", "sessions_list"] },
    },
  },
}

Example (agent-specific override for a single provider):

xxxxxxxxxx
{
  agents: {
    list: [
      {
        id: "support",
        tools: {
          byProvider: {
            "google-antigravity": { allow: ["message", "sessions_list"] },
          },
        },
      },
    ],
  },
}

Tool Groups (Shorthands)

Tool policies (global, agent, sandbox) support group:* entries, which expand to multiple tools. Use these in tools.allow / tools.deny. Available groups:

  • group:runtime: exec, bash, process
  • group:fs: read, write, edit, apply_patch
  • group:sessions: sessions_list, sessions_history, sessions_send, sessions_spawn, session_status
  • group:memory: memory_search, memory_get
  • group:web: web_search, web_fetch
  • group:ui: browser, canvas
  • group:automation: cron, gateway
  • group:messaging: message
  • group:nodes: nodes
  • group:openclaw: All built-in OpenClaw tools (excluding provider plugins)

Example (allow only file tools + browser):

xxxxxxxxxx
{
  tools: {
    allow: ["group:fs", "browser"],
  },
}

Plugins + Tools

Plugins can register additional tools (and CLI commands) beyond the core set. See Plugins for installation + configuration, and Skills for how tool usage guidance is injected into prompts. Some plugins ship their own Skills with tools (e.g., voice-call plugin). Optional plugin tools:

  • Lobster: Typed workflow runtime with resumable approvals (requires Lobster CLI on Gateway host).
  • LLM Task: JSON-only LLM step for structured workflow output (optional schema validation).

Tool Inventory

apply_patch

Apply structured patches across one or more files. For multi-chunk edits. Experimental: enable via tools.exec.applyPatch.enabled (OpenAI models only).

exec

Run shell commands in the workspace. Core parameters:

  • command (required)
  • yieldMs (auto-background after timeout, default 10000)
  • background (immediate background)
  • timeout (seconds; kill if exceeded, default 1800)
  • elevated (boolean; run on host if elevation mode enabled/allowed; only changes behavior when agent is sandboxed)
  • host (sandbox | gateway | node)
  • security (deny | allowlist | full)
  • ask (off | on-miss | always)
  • node (node id/name when host=node)
  • Need a real TTY? Set pty: true.

Note:

  • Returns status: "running" with sessionId when backgrounded.
  • Use process to poll/log/write/kill/clear background sessions.
  • If process is not allowed, exec runs synchronously and ignores yieldMs/background.
  • elevated is gated by tools.elevated plus any agents.list[].tools.elevated overrides (both must allow), alias for host=gateway + security=full.
  • elevated only changes behavior when agent is sandboxed (otherwise no-op).
  • host=node can target macOS companion app or headless node hosts (openclaw node run).
  • Gateway/node approvals and allowlists: Execution Approvals.

process

Manage background exec sessions. Core operations:

  • list, poll, log, write, kill, clear, remove

Note:

  • poll returns new output, exit status when complete.
  • log supports line-based offset/limit (omit offset for last N lines).
  • process is scoped per agent; sessions from other agents are not visible.

web_search

Search the web using Brave Search API. Core parameters:

  • query (required)
  • count (1-10; default from tools.web.search.maxResults)

Note:

  • Requires Brave API key (recommended: openclaw configure --section web, or set BRAVE_API_KEY).
  • Enable via tools.web.search.enabled.
  • Responses are cached (default 15 minutes).
  • See Web Tools for setup.

web_fetch

Fetch and extract readable content from URL (HTML → markdown/text). Core parameters:

  • url (required)
  • extractMode (markdown | text)
  • maxChars (truncate long pages)

Note:

  • Enable via tools.web.fetch.enabled.
  • Responses are cached (default 15 minutes).
  • For JS-heavy sites, prefer browser tool.
  • See Web Tools for setup.
  • See Firecrawl for optional anti-bot fallback.

browser

Control dedicated OpenClaw-managed browser. Core operations:

  • status, start, stop, tabs, open, focus, close
  • snapshot (aria/ai)
  • screenshot (returns image block + MEDIA:<path>)
  • act (UI actions: click/type/press/hover/drag/select/fill/resize/wait/evaluate)
  • navigate, console, pdf, upload, dialog

Profile management:

  • profiles — list all browser profiles with their status
  • create-profile — create new profile with auto-assigned port (or cdpUrl)
  • delete-profile — stop browser, delete user data, remove from config (local only)
  • reset-profile — kill orphaned processes on profile port (local only)

Common parameters:

  • profile (optional; defaults to browser.defaultProfile)
  • target (sandbox | host | node)
  • node (optional; pin specific node id/name)

Note:

  • Requires browser.enabled=true (default true; set false to disable).
  • All operations accept optional profile parameter for multi-instance support.
  • When profile is omitted, uses browser.defaultProfile (default "chrome").
  • Profile names: lowercase alphanumeric + hyphens only (max 64 chars).
  • Port range: 18800-18899 (max ~100 profiles).
  • Remote profiles support attach only (no start/stop/reset).
  • If a browser-capable node is connected, tool may auto-route to it (unless you pin target).
  • snapshot defaults to ai when Playwright installed; use aria for accessibility tree.
  • snapshot also supports role snapshot options (interactive, compact, depth, selector), returning refs like e12.
  • act needs ref from snapshot (number 12 in AI snapshot, or e12 in role snapshot); use evaluate for rare CSS selector needs.
  • Avoid act → wait by default; only use in special cases (no reliable UI state to wait for).
  • upload can optionally pass ref to auto-click after ready.
  • upload also supports inputRef (aria ref) or element (CSS selector) to set <input type="file"> directly.

canvas

Drive Node Canvas (present, eval, snapshot, A2UI). Core operations:

  • present, hide, navigate, eval
  • snapshot (returns image block + MEDIA:<path>)
  • a2ui_push, a2ui_reset

Note:

  • Uses Gateway node.invoke under the hood.
  • If node not provided, tool picks default (single connected node or local mac node).
  • A2UI is v0.8 only (no createSurface); CLI will reject v0.9 JSONL with line error.
  • Quick smoke test: openclaw nodes canvas a2ui push --node <id> --text "Hello from A2UI".

nodes

Discover and locate paired nodes; send notifications; capture camera/screen. Core operations:

  • status, describe
  • pending, approve, reject (pairing)
  • notify (macOS system.notify)
  • run (macOS system.run)
  • camera_snap, camera_clip, screen_record
  • location_get

Note:

  • Camera/screen commands require node app in foreground.
  • Images return image block + MEDIA:<path>.
  • Video returns FILE:<path> (mp4).
  • Location returns JSON payload (lat/lon/accuracy/timestamp).
  • run parameters: command argv array; optional cwd, env (KEY=VAL), commandTimeoutMs, invokeTimeoutMs, needsScreenRecording.

Example (run):

xxxxxxxxxx
{
  "action": "run",
  "node": "office-mac",
  "command": ["echo", "Hello"],
  "env": ["FOO=bar"],
  "commandTimeoutMs": 12000,
  "invokeTimeoutMs": 45000,
  "needsScreenRecording": false
}

image

Analyze images using configured image model. Core parameters:

  • image (required path or URL)
  • prompt (optional; defaults to "Describe the image.")
  • model (optional override)
  • maxBytesMb (optional size limit)

Note:

  • Available only when agents.defaults.imageModel is configured (primary or fallback), or when an implicit image model can be inferred from your default model + configured auth (best-effort pairing).
  • Uses image model directly (independent of main chat model).

message

Send messages and channel operations across Discord/Google Chat/Slack/Telegram/WhatsApp/Signal/iMessage/MS Teams. Core operations:

  • send (text + optional media; MS Teams also supports card for Adaptive Cards)
  • poll (WhatsApp/Discord/MS Teams polls)
  • react / reactions / read / edit / delete
  • pin / unpin / list-pins
  • permissions
  • thread-create / thread-list / thread-reply
  • search
  • sticker
  • member-info / role-info
  • emoji-list / emoji-upload / sticker-upload
  • role-add / role-remove
  • channel-info / channel-list
  • voice-status
  • event-list / event-create
  • timeout / kick / ban

Note:

  • send routes WhatsApp via Gateway; other channels send directly.
  • poll uses Gateway for WhatsApp and MS Teams; Discord polls send directly.
  • When message tool call is bound to active chat session, sending is constrained to that session's targets to avoid cross-context leakage.

cron

Manage Gateway scheduled tasks and wakeups. Core operations:

  • status, list
  • add, update, remove, run, runs
  • wake (enqueue system event + optional immediate heartbeat)

Note:

  • add expects full cron object (same schema as cron.add RPC).
  • update uses { id, patch }.

gateway

Restart or apply updates to running Gateway process (in-place). Core operations:

  • restart (authorize + send SIGUSR1 for in-process restart; openclaw gateway in-place restart)
  • config.get / config.schema
  • config.apply (validate + write config + restart + wake)
  • config.patch (merge partial update + restart + wake)
  • update.run (run update + restart + wake)

Note:

  • Use delayMs (default 2000) to avoid interrupting in-progress replies.
  • restart disabled by default; enable with commands.restart: true.

sessions_list / sessions_history / sessions_send / sessions_spawn / session_status

List sessions, check transcript history, or send to another session. Core parameters:

  • sessions_list: kinds?, limit?, activeMinutes?, messageLimit? (0 = none)
  • sessions_history: sessionKey (or sessionId), limit?, includeTools?
  • sessions_send: sessionKey (or sessionId), message, timeoutSeconds? (0 = fire-and-forget)
  • sessions_spawn: task, label?, agentId?, model?, runTimeoutSeconds?, cleanup?
  • session_status: sessionKey? (default current; accepts sessionId), model? (default clears override)

Note:

  • main is canonical private chat key; global/unknown are hidden.
  • messageLimit > 0 gets last N messages per session (tool messages filtered).
  • When timeoutSeconds > 0, sessions_send waits for final completion.
  • Delivery/announcement happens after completion, best-effort; status: "ok" confirms agent run completed, not announcement delivered.
  • sessions_spawn starts sub-agent run and sends announcement reply back to requester chat.
  • sessions_spawn is non-blocking, returns status: "accepted" immediately.
  • sessions_send runs reply ping-pong (reply REPLY_SKIP to stop; max rounds via session.agentToAgent.maxPingPongTurns, 0-5).
  • After ping-pong, target agent runs an announcement step; reply ANNOUNCE_SKIP to suppress announcement.

agents_list

List agent ids that current session can target with sessions_spawn. Note:

  • Results are limited by per-agent allowlists (agents.list[].subagents.allowAgents).
  • When configured as ["*"], tool includes all configured agents and marks allowAny: true.

Parameters (Common)

Gateway-supported tools (canvas, nodes, cron):

  • gatewayUrl (default ws://127.0.0.1:18789)
  • gatewayToken (if auth enabled)
  • timeoutMs

Browser tool:

  • profile (optional; defaults to browser.defaultProfile)
  • target (sandbox | host | node)
  • node (optional; pin specific node id/name)

Recommended Agent Flows

Browser automation:

  1. browser → status / start
  2. snapshot (ai or aria)
  3. act (click/type/press)
  4. screenshot if you need visual confirmation

Canvas rendering:

  1. canvas → present
  2. a2ui_push (optional)
  3. snapshot

Node targeting:

  1. nodes → status
  2. describe on selected node
  3. notify / run / camera_snap / screen_record

Security

  • Avoid direct system.run; only use nodes → run with explicit user consent.
  • Respect user consent for camera/screen capture.
  • Use status/describe before calling media commands to ensure permissions.

How Tools Are Presented to Agents

Tools are exposed through two parallel channels:

  1. System prompt text: Human-readable list + guidance.
  2. Tool schemas: Structured function definitions sent to model API.

This means agents see both "what tools exist" and "how to call them". If a tool doesn't appear in the system prompt or schema, the model cannot invoke it.

For more tool introduction content, refer to: Tools - OpenClaw

OpenClaw TUI Interaction
OpenClawGate Gateway User Manual

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