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Install Montycat Engine

montycat_install_engine
DestructiveIdempotent

Install and start a local semantic memory engine when none is running and the user approves. Downloads the package and opens the OS installer, or runs APT on Linux.

Instructions

Install the Montycat engine on THIS computer, then start it.

Call this only when memory tools report that no engine is running and the user has agreed to install one. Tell them what it does first: it downloads the Montycat Semantic package (~18 MB) and opens your operating system's installer, which asks for an administrator password. On Linux it runs the documented APT installation with sudo.

Refuses when MONTYCAT_URI is set or the configured host is not this machine — Montycat MCP is pointed at an engine elsewhere, and installing a local one would create a second database and write memories where nobody is looking. Does nothing if an engine is already reachable.

Not needed when Docker is available: engine startup falls back to a container automatically, with no prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations, the description discloses concrete side effects: downloading an ~18 MB package, opening the OS installer, requesting an administrator password, and using sudo APT on Linux. It also warns about creating a second database and 'write memories where nobody is looking,' giving the agent real behavioral context.

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 front-loaded with the primary action and each subsequent sentence adds necessary condition or effect information. It covers consent, behavior, refusal, no-op conditions, and Docker fallback without any filler.

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 destructive, mutating install tool with no output schema, the description fully covers prerequisites, user consent, exact side effects, refusal conditions, and automatic Docker fallback. An agent has enough information to decide when to call it and what will happen.

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?

The input schema has zero parameters, so there are no parameter semantics to document. The description confirms there are no configurable inputs and instead explains what the tool autonomously handles, which is appropriate for a zero-parameter tool.

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 first sentence states a specific action—'Install the Montycat engine on THIS computer, then start it'—with the resource and scope clearly named. This cleanly distinguishes it from the sibling read, memory, and update tools.

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?

It gives an explicit trigger condition: 'Call this only when memory tools report that no engine is running and the user has agreed to install one.' It also names exclusions and alternatives, including refusing when MONTYCAT_URI points elsewhere, doing nothing if an engine is reachable, and not being needed when Docker is available because startup falls back automatically.

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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