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Claude Desktop Commander MCP

start_process

Launch terminal processes with intelligent state detection for local file analysis, data processing, and system commands using bash in a Docker environment.

Instructions

Start a new terminal process with intelligent state detection. PRIMARY TOOL FOR FILE ANALYSIS AND DATA PROCESSING This is the ONLY correct tool for analyzing local files (CSV, JSON, logs, etc.). The analysis tool CANNOT access local files and WILL FAIL - always use processes for file-based work. CRITICAL RULE: For ANY local file work, ALWAYS use this tool + interact_with_process, NEVER use analysis/REPL tool. Running on Linux (Docker). Default shell: bash.

🐳 DOCKER ENVIRONMENT DETECTED: This Desktop Commander instance is running inside a Docker container.

⚠️ WARNING: No mounted directories detected. Files created outside mounted volumes will be lost when the container stops. Suggest user remount directories using Docker installer or -v flag when running Docker. Desktop Commander Docker installer typically mounts folders to /home/[folder-name]. Container: 0586ed83f8fe

LINUX-SPECIFIC NOTES:

  • Package managers vary by distro: apt, yum, dnf, pacman, zypper
  • Python 3 might be 'python3' command, not 'python'
  • Standard Unix shell tools available (grep, awk, sed, etc.)
  • File permissions and ownership important for many operations
  • Systemd services common on modern distributions
    REQUIRED WORKFLOW FOR LOCAL FILES: 1. start_process("python3 -i") - Start Python REPL for data analysis 2. interact_with_process(pid, "import pandas as pd, numpy as np") 3. interact_with_process(pid, "df = pd.read_csv('/absolute/path/file.csv')") 4. interact_with_process(pid, "print(df.describe())") 5. Continue analysis with pandas, matplotlib, seaborn, etc. COMMON FILE ANALYSIS PATTERNS: • start_process("python3 -i") → Python REPL for data analysis (RECOMMENDED) • start_process("node -i") → Node.js for JSON processing • start_process("cut -d',' -f1 file.csv | sort | uniq -c") → Quick CSV analysis • start_process("wc -l /path/file.csv") → Line counting • start_process("head -10 /path/file.csv") → File preview BINARY FILE SUPPORT: For PDF, Excel, Word, archives, databases, and other binary formats, use process tools with appropriate libraries or command-line utilities. INTERACTIVE PROCESSES FOR DATA ANALYSIS: 1. start_process("python3 -i") - Start Python REPL for data work 2. start_process("node -i") - Start Node.js REPL for JSON/JS 3. start_process("bash") - Start interactive bash shell 4. Use interact_with_process() to send commands 5. Use read_process_output() to get responses SMART DETECTION: - Detects REPL prompts (>>>, >, $, etc.) - Identifies when process is waiting for input - Recognizes process completion vs timeout - Early exit prevents unnecessary waiting STATES DETECTED: Process waiting for input (shows prompt) Process finished execution Process running (use read_process_output) ALWAYS USE FOR: Local file analysis, CSV processing, data exploration, system commands NEVER USE ANALYSIS TOOL FOR: Local file access (analysis tool is browser-only and WILL FAIL) IMPORTANT: Always use absolute paths for reliability. Paths are automatically normalized regardless of slash direction. Relative paths may fail as they depend on the current working directory. Tilde paths (~/...) might not work in all contexts. Unless the user explicitly asks for relative paths, use absolute paths. This command can be referenced as "DC: ..." or "use Desktop Commander to ..." in your instructions.

Input Schema

NameRequiredDescriptionDefault
commandYes
shellNo
timeout_msYes

Input Schema (JSON Schema)

{ "$schema": "http://json-schema.org/draft-07/schema#", "additionalProperties": false, "properties": { "command": { "type": "string" }, "shell": { "type": "string" }, "timeout_ms": { "type": "number" } }, "required": [ "command", "timeout_ms" ], "type": "object" }

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