re-ios

run_skill_pipeline

Execute shell-based static analysis pipeline on iOS IPA, .app, or Mach-O binaries to unpack, fingerprint, analyze, scan classes and APIs, and generate a report.

Instructions

Run the full original SKILL.md static analysis pipeline (bash scripts).

Executes the shell-based analysis pipeline from the ios-reverse-engineering skill: unpack → fingerprint → macho analyze → class scan → api scan → report. All results are written to output_dir.

Args: target: Path to IPA file, .app bundle, or Mach-O binary output_dir: Output directory (default: ios_analysis_out)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "target": "<target>"
    },
    "name": "run_skill_pipeline"
  }
}

Input Schema

Name	Required	Description	Default
`target`	Yes
`output_dir`	No		ios_analysis_out

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Jul 9, 2026

TDQS

A3.6/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but only states that results are written to output_dir. It does not disclose side effects (e.g., file creation, modification), environment requirements, or potential destructive operations. Basic pipeline execution is implied but not detailed.

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

Conciseness4/5

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

The description is well-structured with purpose first, then pipeline steps, then argument list. It is appropriately sized for the tool's complexity, with no wasted words. Minor tightening possible.

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

Completeness3/5

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

The description covers basic usage and parameters but lacks output format details, prerequisites, or how results are organized in output_dir. Given no output schema, additional context on result structure would improve completeness.

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?

Schema coverage is 0%, so description adds meaning: target is clarified as 'Path to IPA file, .app bundle, or Mach-O binary' and output_dir has a default value explained. This goes beyond the schema's bare types and provides actionable guidance.

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 'Run the full original SKILL.md static analysis pipeline' and lists the steps (unpack → fingerprint → macho analyze → class scan → api scan → report). This verb+resource combination is specific and distinguishes from sibling tools that perform individual analysis steps.

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

Usage Guidelines3/5

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

The description implies that this tool runs the entire pipeline in one go, contrasting with sibling tools that are single steps. However, it does not explicitly state when to use this vs. alternatives, nor does it mention any prerequisites or caveats.

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