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killbottleneck-mcp

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create_rule

Create automation rules on a map: define WHEN triggers and optional AND conditions, then DO actions in order. Rules run for future events only.

Instructions

Create an automation rule on a map: WHEN trigger fires (and optional AND conditions match) DO the actions in order. Rules run for changes made in the UI, via API and by agents alike. Structural limits: 50 rules per map, 10 actions, 20 conditions — there is NO monthly run quota. A rule applies only to future events, never retroactively. node_id scopes the rule to one node (required for schedule rules whose actions target a node).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesHuman-readable rule name (max 120 chars)
map_idYes
actionsYes1-10 actions, executed in order
enabledNoDefault true
node_idNoOptional: scope the rule to one node of the map
triggerYes
conditionsNoOptional AND chain checked on the trigger node

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.33.2-beta

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It reveals important non-obvious traits: structural limits (50 rules per map, 10 actions, 20 conditions), the absence of a monthly run quota, forward-only applicability, and the node_id scoping requirement for schedule rules. This exceeds what one would expect from a typical description.

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 five sentences, each contributing unique value: the core purpose, cross-UI/API/agent behavior, limits/quota, forward-only semantics, and the crucial node_id requirement. It is front-loaded and free of fluff or redundancy.

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

Completeness4/5

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

This is a complex, nested tool with no output schema, yet the description covers key constraints and behavioral rules (limits, quota, future events, node_id requirement) that an agent needs before invoking. It omits any mention of return values, which would be helpful given the absence of an output schema, but the overall context is sufficient for selection.

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?

The input schema already covers 71% of parameters with detailed descriptions, so the description need not repeat them. The added clarification that node_id is 'required for schedule rules whose actions target a node' is genuinely useful, but the description does not compensate for the remaining 29% of parameters lacking schema descriptions (e.g., map_id, enabled).

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 opens with 'Create an automation rule on a map' and explains the WHEN/DO structure, making the tool's purpose unmistakable. It clearly differentiates from sibling tools like list_rules, update_rule, and delete_rule by focusing on creation.

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

Usage Guidelines3/5

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

The description provides relevant context such as 'Rules run for changes made in the UI, via API and by agents alike' and 'applies only to future events, never retroactively,' which helps an agent understand when creating a rule is appropriate. However, it does not explicitly contrast with alternatives like update_rule or list_rule_templates, leaving room for ambiguity.

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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