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NikitaDatar

Vector Decisions MCP Server

by NikitaDatar

kill_switch

Halt an active AI agent immediately by issuing a kill-switch directive, specifying reason, severity, and shutdown scope for emergency control.

Instructions

Emergency halt for an AI agent. Issues a kill-switch directive to immediately stop agent operations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoScope of shutdownfull
reasonYesReason for triggering kill switch
severityYesSeverity level
agentNameYesName of the AI agent to halt
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'immediately stops agent operations,' which is a key behavior, but it does not disclose potential consequences such as irreversibility, data loss, or interruption of ongoing tasks. For a kill switch, this is a significant omission, yet the immediate-halt nature is partially transparent. Thus a score of 3 reflects adequate but incomplete disclosure.

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 two sentences long, with the purpose front-loaded and no wasted words. It states the nature (emergency halt) and the action (immediately stop operations) efficiently. Every sentence earns its place and the structure is optimal for quick agent comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's destructive nature (kill switch) and the absence of an output schema, the description is arguably thin. It tells agents what it does but not the consequences, the meaning of severity levels, or any prerequisites. While the schema fills in parameter details, the overall context—especially side effects—remains incomplete. A score of 3 reflects that it's minimally adequate but could be more thorough for a high-impact action.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (agentName, reason, severity, scope) with meaningful descriptions. The tool description adds no extra parameter information, so it does not go beyond the baseline. A score of 3 is appropriate because the schema does the heavy lifting, and the description does not compensate with any additional semantics.

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 tool's function: 'Emergency halt for an AI agent' and 'immediately stop agent operations.' This is a specific verb (halt/stop) and resource (AI agent), and it is clearly distinct from sibling tools like trust score, compliance check, or risk assessment, which serve different purposes. The name reinforces the intent, making it unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates the tool is for emergency situations ('Emergency halt'), giving clear context for when to use it. However, it does not explicitly mention alternatives or when not to use it. Since sibling tools are entirely different domains (trust, compliance, risk, assurance, decision), there's no real ambiguity, but explicit exclusions are missing, so it's slightly below a perfect score.

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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