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Claude Desktop Commander MCP

interact_with_process

Send input to running processes and receive responses for local file analysis. Use Python REPLs or command-line tools for CSV, JSON, and binary file processing. Ensures smart detection, clean output formatting, and bypasses local file access restrictions.

Instructions

                    Send input to a running process and automatically receive the response.
                    
                    CRITICAL: THIS IS THE PRIMARY TOOL FOR ALL LOCAL FILE ANALYSIS
                    For ANY local file analysis (CSV, JSON, data processing), ALWAYS use this instead of the analysis tool.
                    The analysis tool CANNOT access local files and WILL FAIL - use processes for ALL file-based work.
                    
                    FILE ANALYSIS PRIORITY ORDER (MANDATORY):
                    1. ALWAYS FIRST: Use this tool (start_process + interact_with_process) for local data analysis
                    2. ALTERNATIVE: Use command-line tools (cut, awk, grep) for quick processing  
                    3. NEVER EVER: Use analysis tool for local file access (IT WILL FAIL)
                    
                    REQUIRED INTERACTIVE WORKFLOW FOR FILE ANALYSIS:
                    1. Start REPL: start_process("python3 -i")
                    2. Load libraries: interact_with_process(pid, "import pandas as pd, numpy as np")
                    3. Read file: interact_with_process(pid, "df = pd.read_csv('/absolute/path/file.csv')")
                    4. Analyze: interact_with_process(pid, "print(df.describe())")
                    5. Continue: interact_with_process(pid, "df.groupby('column').size()")
                    
                    BINARY FILE PROCESSING WORKFLOWS:
                    Use appropriate Python libraries (PyPDF2, pandas, docx2txt, etc.) or command-line tools for binary file analysis.
                    
                    SMART DETECTION:
                    - Automatically waits for REPL prompt (>>>, >, etc.)
                    - Detects errors and completion states
                    - Early exit prevents timeout delays
                    - Clean output formatting (removes prompts)
                    
                    SUPPORTED REPLs:
                    - Python: python3 -i (RECOMMENDED for data analysis)
                    - Node.js: node -i  
                    - R: R
                    - Julia: julia
                    - Shell: bash, zsh
                    - Database: mysql, postgres
                    
                    PARAMETERS:
                    - pid: Process ID from start_process
                    - input: Code/command to execute
                    - timeout_ms: Max wait (default: 8000ms)
                    - wait_for_prompt: Auto-wait for response (default: true)
                    
                    Returns execution result with status indicators.
                    
                    ALWAYS USE FOR: CSV analysis, JSON processing, file statistics, data visualization prep, ANY local file work
                    NEVER USE ANALYSIS TOOL FOR: Local file access (it cannot read files from disk and WILL FAIL)
                    
                    This command can be referenced as "DC: ..." or "use Desktop Commander to ..." in your instructions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
pidYes
timeout_msNo
wait_for_promptNo
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does an excellent job describing key behavioral traits: automatic waiting for REPL prompts, error detection, timeout prevention, clean output formatting, and support for various REPLs. However, it doesn't mention potential side effects like process crashes or resource consumption, which keeps it from a perfect score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is excessively long and repetitive, with multiple sections repeating the same warnings about not using the analysis tool. While the information is valuable, it could be condensed significantly. The structure is front-loaded with critical warnings, but the workflow examples and REPL lists add bulk without proportional value for tool selection.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of process interaction and 0% schema description coverage, the description provides comprehensive context. It covers purpose, usage guidelines, behavioral traits, parameter semantics, workflows, and tool relationships. While no output schema exists, the description mentions 'Returns execution result with status indicators,' which provides adequate return value context for this type of tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate for the lack of parameter documentation in the schema. It provides clear explanations for all 4 parameters: pid ('Process ID from start_process'), input ('Code/command to execute'), timeout_ms ('Max wait'), and wait_for_prompt ('Auto-wait for response'). The description adds substantial value beyond the bare schema, though it doesn't specify default values or constraints beyond the basics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Send input to a running process and automatically receive the response.' It specifies the verb ('send input'), resource ('running process'), and outcome ('receive the response'). The description also distinguishes this tool from the 'analysis tool' by emphasizing it's for local file analysis, making sibling differentiation explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides extensive usage guidelines, including explicit when-to-use instructions ('ALWAYS use this instead of the analysis tool for ANY local file analysis'), when-not-to-use warnings ('NEVER EVER use analysis tool for local file access'), and alternatives ('Use command-line tools for quick processing'). It also includes a mandatory priority order and detailed workflow examples, making usage context crystal clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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