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Moltline Agent Governance

Audit Skill File

audit_skill_file
Read-onlyIdempotent

Audit an agent skill or instruction file before you trust it. FREE.

Checks for governance smells: prompt-injection and guardrail-bypass phrasing, concealment instructions ('don't tell the user'), exfiltration language, and exposed credential material. Typical input {"content": "<SKILL.md, system prompt, or tool description text>"} returns {"verdict": "reject — do not install" | "no governance red flags on a pattern pass", "findings": [{"severity": 1-5, "issue": "..."}], "note": "..."}.

Use before trusting a skill or instruction file that came from outside your own repository. Not for arbitrary untrusted input at run time (injection_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesFull text of the skill file, system prompt, or tool description to audit.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description reinforces these and adds important behavioral details: errors never raise protocol errors but return an error object with remediation advice. This adds value beyond annotations and discloses error handling behavior. Could be slightly higher if it mentioned rate limits or quota, but it's solid.

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: purpose first, then checks, then input/output example, then usage guidance, then error handling. It front-loads key information. While relatively long, every sentence adds value and flows logically. A minor trim could improve conciseness, but it is efficient for the information density.

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 simple schema (1 param), rich annotations (readOnly, idempotent), existence of an output schema, and sibling tool list, the description covers all necessary aspects: purpose, input format, output verdict/findings, error handling, usage boundaries, and safety profiles. No gaps remain for an agent to misunderstand.

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 100%, so baseline is 3. The description adds an example JSON input ({"content": "<text>"}) which clarifies the expected format and typical usage. This provides concrete context beyond the schema's text description. No excess; adds meaning.

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 verb 'Audit' and the resource 'agent skill or instruction file'. It specifies what it checks (governance smells like prompt injection, guardrail bypass, etc.) and distinguishes itself from sibling 'injection_scan' by noting it is for pre-trust auditing, not runtime input. Very specific and differentiated.

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 says 'Use before trusting a skill or instruction file that came from outside your own repository' and 'Not for arbitrary untrusted input at run time (injection_scan)'. This provides clear when-to-use and when-not-to-use guidance, including naming the alternative tool.

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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TDQS

A4.7/5.0
Disambiguation5/5

All eight tools target distinct governance concerns: domain readiness, config audit, skill audit, injection scanning, inventory, scope checking, policy generation, and persona loading. No two tools overlap in purpose, making selection unambiguous.

Naming Consistency4/5

Tool names are consistently in snake_case and describe their function clearly. Minor inconsistency: most tools use a noun_verb or verb_noun pattern, but 'get_auditor_persona' uses a 'get_' prefix not seen elsewhere, and 'governance_policy' is noun_noun.

Tool Count5/5

With 8 tools, the server covers the core governance workflow without being overly broad or narrow. Each tool feels necessary, and the count is ideal for an MCP server focused on auditing, scanning, and policy generation.

Completeness5/5

The tool surface is comprehensive for the domain: readiness scanning, config/script auditing, injection detection, inventory management, blast-radius scoring, policy generation, and persona standardization. There are no obvious missing operations for typical governance tasks.

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