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Remediation Plan

remediate
Read-onlyIdempotent

Scan AI agent configurations and Docker images for vulnerabilities, then generate actionable upgrade commands and credential fixes to remediate issues.

Instructions

Generate a remediation plan for vulnerabilities in your AI agent setup.

    Scans for vulnerabilities, then generates actionable fix commands for
    each affected package (npm install, pip install), credential scope
    reduction guidance, and reports on unfixable vulnerabilities.

    Args:
        config_path: Path to a specific MCP config directory.
                     If not provided, auto-discovers all local agent configs.
        image: Docker image reference to scan (e.g. "nginx:1.25").

    Returns:
        JSON with package_fixes (upgrade commands by ecosystem),
        credential_fixes (scope reduction steps), and unfixable items.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
config_pathNoPath to MCP client config directory. Auto-discovers all if omitted.
imageNoDocker image to scan, e.g. 'nginx:1.25'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations indicate readOnly and idempotent, and the description confirms the tool scans and returns a plan without side effects, aligning with annotations. It adds detail on output structure but does not cover all behavioral nuances.

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?

Description is concise and well-structured with clear sections for purpose, args, and returns. It is front-loaded and informative without being verbose.

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?

Given the complexity and presence of an output schema, the description covers primary inputs and outputs. It could mention prerequisites or optional dependencies for scanning, but it is adequate.

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

Parameters4/5

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

Both parameters have full schema descriptions, and the description adds context about auto-discovery for config_path, providing value 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?

Description clearly states the tool 'Generate[s] a remediation plan for vulnerabilities in your AI agent setup' and differentiates from sibling scan tools by focusing on actionable fixes and credential guidance.

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?

The description explains what the tool does but does not explicitly state when to use it versus sibling tools like scans or checks. Usage context is implied but not formalized.

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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