Scans a project directory for npm and pip manifests, then checks listed packages against real registries to detect slopsquatting risks.
Auto-detect and check both npm and pip manifests (package.json, requirements.txt, pyproject.toml) in a directory against their real registries
| Name | Required | Description | Default |
|---|---|---|---|
| directory | Yes | Path to the project root directory |
Changes observed during successful MCP inspections.
v0.1.5Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"v0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does usefully disclose two traits: automatic detection (so missing manifest types are tolerated) and live lookups 'against their real registries', which signals outbound network calls. It does not disclose what findings look like, timeout/failure behavior, or whether the operation touches anything on disk.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One dense sentence with zero filler, and the key differentiator (both npm and pip) is front-loaded. Slightly over-packed with parenthetical file lists but still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description covers the 'what' and enough of the 'how' (auto-detect, registry lookup) to call it, but leaves return shape and partial/error results unexplained. Adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the single parameter is fully documented as 'Path to the project root directory'. The description reinforces that the directory is a project root by naming the manifest files it looks for, but adds no format, path-style, or recursion details beyond the schema. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action (auto-detect and check manifests) against a specific resource (npm/pip manifests in a directory), and the explicit file names (package.json, requirements.txt, pyproject.toml) make the scope unambiguous. It is implicitly distinguished from scan_npm, scan_pip, and check_package by covering both ecosystems across a whole directory, but it never names those siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied rather than stated: use it when you have a project root containing one or both manifest types. There is no explicit 'use this instead of scan_npm/scan_pip when...' guidance, and no mention of what happens when no manifest is found.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.