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websex_bulk_extractor

Run bulk forensic extraction to recover artifacts from disk images and files in an engagement namespace; arguments execute directly without shell parsing.

Instructions

Run bulk extractor from the WebSex forensics toolkit. The container executes bulk_extractor directly with the supplied argv tokens; shell parsing is never used. Runs from the selected engagement namespace; use relative paths for engagement artifacts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stdinNo
argumentsYes
engagement_idYes
working_directoryNo.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the execution model (argv tokens passed directly, shell parsing never used) and the namespace/path semantics, which is genuine behavioral context. It does not cover output behavior, error handling, resource or time limits, or whether extraction can overwrite existing artifacts, so meaningful gaps remain.

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?

Three sentences, no filler, with the core action front-loaded before the execution-model and path-scoping details. Slightly terse relative to the disclosure burden but structurally sound.

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?

An output schema exists, so return values need not be explained. Against that, the tool has no annotations and 0% parameter description coverage, and the description leaves gaps on stdin, error/permission behavior, and selection relative to other extraction tools, making it only minimally complete for a forensics execution tool.

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 coverage is 0% across 4 parameters, so the description must compensate. It implies meaning for arguments (argv tokens), working_directory (relative paths within the engagement), and engagement_id (selected engagement namespace), but leaves stdin completely unexplained and gives no format details for the argv array.

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

Purpose4/5

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

States a specific verb and resource (run bulk_extractor from the WebSex forensics toolkit), which is clearly distinguishable from generic siblings like websex_execute or websex_bash. However, it offers no differentiation from the closely related carving/forensics siblings such as websex_foremost, websex_scalpel, or websex_binwalk, so an agent cannot tell from the description alone why it should pick this one.

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

Usage Guidelines2/5

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

The description gives operational context (runs from the selected engagement namespace, use relative paths for engagement artifacts) but never states when to use this tool versus the many other forensics/extraction siblings. There is no when-not guidance and no named alternative, leaving selection entirely to inference.

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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