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DepShield MCP

DepShield MCP

by devanshkaria88
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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check_dependency

Scan packages for vulnerabilities and verify registry existence before installation to block CVEs and prevent fake or hallucinated npm and PyPI dependencies.

Instructions

Check a package for known vulnerabilities and verify it exists on the registry. MUST be called before installing any dependency.

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": {
      "name": "<name>"
    },
    "name": "check_dependency"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesPackage name (e.g., 'lodash', 'express')
versionNoSpecific version to check. If omitted, checks latest.
ecosystemNoPackage ecosystem. Defaults to npm.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions vulnerability checking and existence verification, but omits critical details like: whether it returns a report or boolean, what happens when vulnerabilities are found (blocking vs. warning), error handling for non-existent packages, or data sources used.

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

Conciseness5/5

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

Two sentences, zero waste. The first sentence front-loads the core action (vulnerability checking + existence verification), while the second provides the critical workflow constraint (MUST call before install). Every word earns its place.

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?

Given the lack of output schema and annotations, the description should ideally characterize the return value (e.g., vulnerability report, risk score, or boolean) to help the agent interpret results. The input side is complete due to schema coverage, but the output side leaves agents uncertain about what data structure to expect.

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?

The schema has 100% description coverage for all 3 parameters (name, version, ecosystem), establishing a baseline of 3. The description mentions 'package' generally but adds no additional semantic context—such as version syntax requirements or ecosystem-specific behaviors—beyond what the schema already provides.

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 checks for 'known vulnerabilities' and verifies registry existence, using specific verbs. It distinguishes from siblings (like audit_project or deep_scan) by focusing on single-package pre-installation verification rather than project-wide auditing.

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

Usage Guidelines4/5

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

The 'MUST be called before installing any dependency' directive provides explicit workflow guidance. However, it lacks explicit comparison to siblings like 'audit_project' (for existing dependencies) or 'deep_scan' (for comprehensive analysis), which would help agents choose between similar security tools.

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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