Moltline Agent Governance & MCP Auditor

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

Name	Required	Description	Default
`fleet_context`	No	Optional plain-language description of the fleet (size, capabilities, sensitive systems) used to tailor the policy; empty returns the generic baseline.

Output Schema

TableJSON Schema

Name	Required	Description	Default
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: error handling behavior (returns error objects instead of protocol errors), retry safety, and a licensing note (PREMIUM). These are valuable disclosures not present in structured fields.

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 concise and well-structured: purpose first, then coverage scope, then input/output example, usage guidelines, error behavior, and idempotency. Every sentence adds value without redundancy.

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 optional tool with an output schema, the description covers all essential aspects: functionality, when to use, output example, error handling, and idempotency. No gaps remain given the context signals.

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% with a clear description for the lone parameter. The description adds a concrete example of typical input, which helps the agent understand the expected format and tailoring. While the baseline is 3 due to high schema coverage, the example and context elevate the score.

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-resource pair ('Generate an audit-ready agent-governance policy for a fleet') and explicitly differentiates from siblings by stating what the tool is not for (inventory_report, audit_mcp_config).

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?

Clearly states when to use ('Use when a fleet needs a written policy document') and when not to use, naming specific alternative tools. This provides explicit guidance for agent decision-making.

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

A4.4/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose targeting a different aspect of agent governance: domain readiness, config auditing, skill file auditing, injection scanning, inventory, blast radius scoring, policy generation, and persona loading. Each description explicitly states what the tool is not for, eliminating ambiguity.

Naming Consistency2/5

Naming is inconsistent: some tools start with a verb (audit_mcp_config, get_auditor_persona), others with a noun (agent_readiness_scan, injection_scan, inventory_report, scope_check), and governance_policy is a plain noun phrase with no verb. There is no predictable pattern across the set.

Tool Count5/5

8 tools is well-scoped for the domain of agent governance and auditing. Each tool serves a distinct and necessary function without redundancy, covering readiness, configuration, skill files, injection, inventory, permissions, policy, and persona.

Completeness4/5

The tool surface covers the major governance activities: audit, scan, inventory, policy generation, and blast radius. A minor gap is the lack of a tool to verify compliance against a generated policy, but the existing audit tools can be used for that purpose.

Resources

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