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princeruhulofficial

mcp-agent-enterprise-guard

generate_audit_report_tool

Turn logs and optional scores into markdown audit reports, delivering clear governance insights and auditability for AI agent operations.

Instructions

Generate a markdown governance/audit report from logs (and optional pre-computed scores).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
logsYes
scoresNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions the output is 'markdown governance/audit report' and that it uses logs and optional scores, but it does not disclose side effects, permissions, or any specific behaviors beyond generation. Since generation is inherently non-destructive, this is a minor gap; however, additional context about the report's contents or how scores are integrated would improve transparency.

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 a single, front-loaded sentence that directly states the action and resource. There is no wasted text, and it conveys all essential information efficiently.

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?

For a simple report-generation tool with an output schema, the description covers the key inputs and output format. It could be more complete by referencing the sibling tools as pipeline steps or clarifying the report's scope, but given its simplicity and the presence of an output schema, it's sufficiently complete.

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 description coverage is 0%, so the description must compensate. It does by naming both parameters: 'logs' (the data source) and 'pre-computed scores' (optional input). This clarifies that 'logs' is the primary input and 'scores' is an optional enhancement, adding meaning beyond the bare schema definitions.

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's function: 'Generate a markdown governance/audit report from logs (and optional pre-computed scores).' It uses a specific verb ('Generate') and a specific resource ('markdown governance/audit report'), and it distinguishes itself from sibling tools by focusing on report generation rather than detection, scoring, or evaluation.

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 implies the tool is used when you have logs (and optionally pre-computed scores) and need a governance/audit report. While it doesn't explicitly mention alternatives or exclusions, the context of sibling tools (detect, score, evaluate) makes it clear this is the report-generation step. The phrase 'and optional pre-computed scores' hints at a preceding scoring step, providing useful context.

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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