Auto-fix drift between docs and reality (process counts, versions, port numbers). Use dry_run=true to preview changes.
| Name | Required | Description | Default |
|---|---|---|---|
| dry_run | No | If true, only report drift without fixing. Default: true |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate idempotent and not destructive. The description adds behavioral context: it auto-fixes drift and allows preview via dry_run. No contradictions, but could mention reversibility or permission requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff, front-loaded with purpose. Every word contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers purpose, usage hint, and examples. However, it does not define 'reality' (e.g., system state vs. other docs), which could cause ambiguity. Given simplicity, it's fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the dry_run parameter fully. The description repeats the schema's meaning ('preview changes') without adding new semantics. With 100% coverage, baseline is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Auto-fix drift between docs and reality', with specific examples (process counts, versions, port numbers). This distinguishes it from siblings like drift_audit, which likely only reports drift.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a usage guideline: 'Use dry_run=true to preview changes.' However, it does not explicitly state when to use this tool versus alternatives (e.g., drift_audit for auditing only), leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but there is some overlap among multiple audit/check tools (e.g., accountability_check, drift_audit, security_audit). However, descriptions and naming help differentiate their specific scopes, so ambiguity is low.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_archive_safety, propagate_family_member), making them predictable and easy to understand.
33 tools is on the high side, but the server covers a broad domain (system health, audits, propagation, session management, etc.). The count is justified by the comprehensive scope, though it may feel heavy for agents.
The tool set covers the entire lifecycle of system management: health monitoring, audits, propagation, sessions, compliance, and more. No obvious gaps for the stated purpose of a nervous system framework.
