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AIops-tools

firewall-aiops

pending_changes

Checks staged firewall rule changes for management-access lockout risk before committing, reporting worst-case findings first with certainty flags to prevent cutting off the endpoint.

Instructions

[READ] The staged rule set apply_changes would commit, with lockout risk.

Run this BEFORE apply_changes. It reports whether committing the staged rules would cut the endpoint this tool manages the firewall through — a disabled 'pass' rule that permits management access, or an enabled 'block' rule that covers it. Findings are ranked worst-first and carry a certain flag: certain ones make apply_changes refuse, uncertain ones (alias destinations, 'any', interface groups) are warnings only and never block.

basis states what this is: the staged rule STATE, not a diff against the running config — neither platform exposes a per-rule dirty flag over REST.

Args: target: Firewall target name from config; omit for the default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNo
Behavior5/5

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

With no annotations provided, the description carries full burden and fully delivers. It discloses the read-only nature ('[READ]'), the certain vs uncertain flag distinction (certain blocks apply_changes, uncertain only warns), and the fallback behavior when no risky rule exists. It also exposes the internal limitation (no per-rule dirty flag over REST).

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?

Three tight paragraphs, each earning its place: purpose/behavior, the certain-vs-uncertain distinction, and the single parameter. Front-loaded with the core READ purpose and usage directive. Zero filler.

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?

Despite being a moderately complex tool (lockout risk semantics, certain/uncertain flags, aliases/interface-group edge cases), the description covers purpose, output semantics, limitations, and parameters. No output schema exists, so describing the certain flag and finding ordering ('ranked worst-first') compensates fully.

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?

Schema coverage is 0%, so the description must compensate for the single 'target' parameter, which it does: 'Firewall target name from config; omit for the default.' This explains the parameter's purpose and default behavior, going meaningfully beyond the bare 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?

The description has a specific verb+resource ('reports whether committing the staged rules would cut the endpoint'), and clearly distinguishes this from siblings like list_rules, rule_detail, and apply_changes. The '[READ]' prefix plus the explicit relationship to apply_changes leaves zero ambiguity about purpose.

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

Usage Guidelines5/5

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

Explicitly instructs 'Run this BEFORE apply_changes' and explains what it does (reports lockout risk). It also distinguishes its behavior from a diff against running config, stating why — neither platform exposes a per-rule dirty flag over REST. This is explicit when-to-use guidance with rationale.

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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