get_framework
Returns the complete nervous system framework - all behavioral rules, guardrails, and enforcement patterns that keep LLMs from hurting themselves.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Returns the complete nervous system framework - all behavioral rules, guardrails, and enforcement patterns that keep LLMs from hurting themselves.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds context that the tool returns the 'complete' framework, which is beneficial beyond annotations.
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?
Single sentence, front-loaded with the key action, no wasted words.
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?
With no parameters, well-defined annotations, and no output schema, the description sufficiently explains what the tool does.
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 input schema has zero parameters, so the description does not need to provide parameter details. The baseline of 4 applies as no compensation is needed.
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 specifies a clear verb ('Returns'), a clear resource ('complete nervous system framework'), and provides scope ('all behavioral rules, guardrails, and enforcement patterns'), distinguishing it from siblings like get_nervous_system_info or guardrail_rules.
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 implies use for retrieving the full framework but does not explicitly state when to use this tool versus alternatives like get_nervous_system_info or guardrail_rules.
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.