Skip to main content
Glama
vmware-skills

vmware-harden

by vmware-skills
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_remediation

Read-onlyIdempotent

Fetch the saved LLM-generated remediation plan for a violation, with execution steps, impact, and confidence. Returns None if no suggestion is available.

Instructions

[READ] Fetch the persisted LLM-generated remediation Suggestion for one violation. Returns {summary, execution_plan.steps, impact_prediction (workload impact, maintenance window, rollback plan), confidence (0.0-1.0), human_review_required}, or None when no advisor suggestion has been generated for that violation yet (generate one via the vmware-harden CLI advisor). Read-only lookup in the local twin DB (~/.vmware-harden/twin.duckdb); no network calls and nothing is executed — suggestions are advisory only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
violation_idYesThe 'id' field of a row returned by list_violations (the violation's own id, not its rule_id or node_id). An id with no stored suggestion returns None rather than an error, so None means "not generated yet", not "not found".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.10.4
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / violation_id / description
      Added value: +"The 'id' field of a row returned by list_violations (the violation's own id, not its rule_id or node_id). An id with no stored suggestion returns None rather than an error, so None means \"not generated yet\", not \"not found\"."
  2. Addedv1.5.29
  3. Removedv1.5.28
  4. First observedv1.5.22

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses that the call is a read-only local DB lookup, makes no network calls, executes nothing, and returns advisory content only. It also explains the None-vs-error behavior, providing meaningful behavioral context that annotations alone would not convey.

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?

The description is front-loaded with '[READ]' and a direct action statement, then efficiently lists return fields, the None edge case, and execution constraints. Every sentence carries useful information without redundancy or padding.

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?

For a single-parameter read tool with a rich output schema, the description covers the input source, the missing-suggestion case, the storage location, and execution safety. Nothing an agent needs to invoke it correctly is left out.

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?

Schema description coverage is 100%, so the baseline is 3. The schema already details that violation_id must be the violation's own id and that missing suggestions return None; the tool description repeats and reinforces this rather than adding entirely new parameter semantics.

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 uses a specific verb ('Fetch') and resource ('persisted LLM-generated remediation Suggestion for one violation'), and it enumerates the return fields. This clearly identifies the tool's action and distinguishes it from sibling list/get tools targeting baselines, rules, or violations.

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 description clarifies that the violation_id must come from list_violations and explains that None means the suggestion has not been generated, with an explicit pointer to the vmware-harden CLI advisor for generation. It does not explicitly contrast against sibling tools, but the usage context is clear and includes a generation pathway.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools