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scout_cves

Read-only

Identify vulnerabilities (CVEs) in Docker images via Docker Scout. Filter by severity, fixed version, or exclude base image CVEs.

Instructions

List vulnerabilities (CVEs) in an image via Docker Scout.

Anonymous scans work for public images; Hub policy enforcement and richer recommendations need docker login on the host running this MCP server.

args: image - Image reference (a tag or a digest) only_fixed - Only report CVEs with a fixed version available only_severity - Filter to severities: "critical", "high", "medium", "low", "unspecified" ignore_base - Exclude CVEs introduced by the base image format - Output format: "json" (default; parsed into the return dict), "sarif", "spdx", "list", "markdown", or "text" platform - Platform of the image to analyze, e.g. "linux/amd64" returns: dict - {"format": , "result": , "raw": }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes
formatNojson
platformNo
only_fixedNo
ignore_baseNo
only_severityNo
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: anonymous scan limitations, need for docker login for policy enforcement, and return format details. No contradictions.

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 an initial purpose statement, authentication note, and a labeled args section. It is slightly longer than necessary but front-loaded and easy to scan.

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

Completeness5/5

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

Given the tool has 6 parameters, no output schema, and moderate complexity, the description covers purpose, usage context, all parameter details, and the return value structure. It provides enough information for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description provides clear explanations for all 6 parameters (image, only_fixed, only_severity, ignore_base, format, platform) including defaults and allowed values, fully compensating for the schema gap.

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?

The description clearly states the tool lists CVEs in an image via Docker Scout. It is specific but does not explicitly differentiate from sibling scout tools like scout_quickview or scout_recommendations, though the purpose stands alone.

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?

Provides context on authentication requirements (anonymous scans vs. login needed for richer recommendations) but does not explicitly state when to use this tool over alternatives or when not to use it.

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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