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@guardbee/mcp-security-proxy

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slopsquat_scanner__scan_pip

Scans requirements.txt or pyproject.toml dependencies against PyPI to flag nonexistent package names and recently published packages, detecting likely hallucinated or malicious dependencies.

Instructions

Check every dependency declared in requirements.txt or pyproject.toml against the real PyPI registry -- flags any package name that doesn't exist at all (a likely LLM-hallucinated dependency an attacker may have pre-registered), and separately notes any that exist but were only first published very recently.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
directoryYesPath to the directory containing requirements.txt or pyproject.toml

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does disclose meaningful detection semantics: it queries the real PyPI registry and reports two distinct finding classes (non-existent names vs. very recently first-published names), which tells the agent what to expect and why. It does not mention network dependency, rate limits, or that the operation is read-only, but the scanner semantics are otherwise well communicated.

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?

One sentence, front-loaded with the core action and its target, and each clause (existence check, recency check) earns its place. The parenthetical explanation of the threat model is slightly verbose but justified.

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

Completeness4/5

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

No output schema or annotations exist, but the description names the two categories of findings it emits, which effectively previews the result content. It stops short of describing the return structure or severity levels, so it is strong but not exhaustive.

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?

There is a single parameter with 100% schema description coverage, so the baseline is 3. The description restates the manifest files it looks for but adds no path syntax, recursion, or error-handling detail beyond the schema.

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 gives a precise verb (check/scan) and resource (every dependency declared in requirements.txt or pyproject.toml), and the pip-ecosystem scoping implicitly separates it from slopsquat_scanner__scan_npm and slopsquat_scanner__check_package. An agent can select it without opening the schema.

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?

Usage is implied by the artifact type it targets (pip manifests), but there is no explicit statement of when to prefer this over scan_npm, check_package, or scan_directory, and no preconditions or exclusions are given.

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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