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interact_with_process

Destructive

Send input to running processes and receive responses for local file analysis, data processing, and interactive REPL workflows.

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)
                    - verbose_timing: Enable detailed performance telemetry (default: false)

                    Returns execution result with status indicators.

                    PERFORMANCE DEBUGGING (verbose_timing parameter):
                    Set verbose_timing: true to get detailed timing information including:
                    - Exit reason (early_exit_quick_pattern, early_exit_periodic_check, process_finished, timeout, no_wait)
                    - Total duration and time to first output
                    - Complete timeline of all output events with timestamps
                    - Which detection mechanism triggered early exit
                    Use this to identify slow interactions and optimize detection patterns.

                    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
pidYes
inputYes
timeout_msNo
wait_for_promptNo
verbose_timingNo
Behavior4/5

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

The description adds significant behavioral context beyond annotations. Annotations indicate destructiveHint=true and openWorldHint=true, but the description elaborates with details like smart detection (waits for REPL prompts, detects errors), performance debugging options with verbose_timing, and specific workflows for file analysis. It doesn't contradict annotations, but provides rich operational context that annotations alone don't cover.

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 (CRITICAL warning, priority order, workflows, binary processing, smart detection, REPLs, parameters, performance debugging, usage reminders) that could be condensed. Sentences like 'ALWAYS USE FOR: CSV analysis, JSON processing...' and 'NEVER USE ANALYSIS TOOL FOR: Local file access...' repeat earlier points, reducing efficiency. It's front-loaded but not concise overall.

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 tool's complexity (interactive process communication with destructive potential) and lack of output schema, the description is highly complete. It covers purpose, usage guidelines, behavioral traits, parameters, examples, supported environments, and debugging options. It addresses the need for context beyond annotations and schema, making it fully adequate for an agent to understand and use the tool correctly.

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

Parameters3/5

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

Schema description coverage is 0%, but the description compensates by listing and briefly explaining all 5 parameters (pid, input, timeout_ms, wait_for_prompt, verbose_timing). It provides default values and usage context (e.g., 'pid: Process ID from start_process'), though it lacks detailed syntax or format specifications. This meets the baseline for adequate parameter information given the coverage gap.

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') and resource ('running process'), and distinguishes it from sibling tools by emphasizing it's the primary tool for local file analysis versus alternatives like the analysis tool or command-line tools.

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 explicit and detailed guidance on when to use this tool versus alternatives. It states: 'CRITICAL: THIS IS THE PRIMARY TOOL FOR ALL LOCAL FILE ANALYSIS' and gives a mandatory priority order (e.g., use this tool first for local data analysis, use command-line tools as alternative, never use analysis tool for local file access). It also includes specific workflow examples and lists supported REPLs.

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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