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Moltline Agent Governance

Governance Policy

governance_policy
Read-onlyIdempotent

Generate an audit-ready agent-governance policy for a fleet. PREMIUM (license).

Covers inventory cadence, ownership rules, least-privilege approval gates, injection defense, logging/retention, and decommissioning triggers. Typical input {"fleet_context": "20 agents, 3 with shell access, one finance bot"} returns {"policy": ..., "sections": {...}, "context_note": ..., "audit_checklist": ["...", ...]}.

Use when a fleet needs a written policy document. Not for assessing what the fleet currently does (inventory_report, audit_mcp_config). 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
fleet_contextNoOptional plain-language description of the fleet (size, capabilities, sensitive systems) used to tailor the policy; empty returns the generic baseline.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds beyond these by stating 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry' and detailing the error response format ('never raises a protocol error — it returns {"error": ...}'). This provides extra behavioral context not present in annotations.

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 structured with clear sections: purpose, premium note, coverage, example, usage guidance, error handling, and safety. It is front-loaded with the main action and every sentence adds value. No unnecessary words.

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 tool's complexity (generating a policy document), the description covers input, output format, error handling, retry safety, and when to use. The existence of an output schema is noted in context, and the description explains the output shape. It is fully self-contained and leaves no ambiguity.

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 100% and the schema already describes the parameter well. The description adds value by providing an example input and showing the output structure, which helps the agent understand how to use the parameter effectively. This goes beyond the baseline of 3.

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 specific verb and resource: 'Generate an audit-ready agent-governance policy for a fleet.' It also distinguishes from siblings by stating that it is not for assessing current fleet state (inventory_report, audit_mcp_config). The purpose is unmistakable.

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 when a fleet needs a written policy document' and provides clear alternatives: 'Not for assessing what the fleet currently does (inventory_report, audit_mcp_config).' It also describes error handling behavior and retry safety, giving the agent a complete usage guide.

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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