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Glama

Offer to turn this server on for the session (the user decides)

local_llm_enable
Idempotent

Prompt the user to activate local processing for privacy-sensitive or large-output tasks, keeping data on-device.

Instructions

Ask the user whether to turn local-llm-mcp on for this session. The server shows the user a dialog; nothing is delegated unless they say yes. Call it once when a step would print far more than you need or would touch private data. If the user declined, do not call it again unless they ask. Returns the operating rules when the user turns it on, or a refusal otherwise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=false, idempotentHint=true, and destructiveHint=false. The description adds behavioral context: 'The server shows the user a dialog; nothing is delegated unless they say yes' and 'Returns the operating rules when the user turns it on, or a refusal otherwise.' This clarifies the interactive nature and consent requirement. It does not contradict any annotation, though it does not address the idempotency hint explicitly.

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 three sentences and front-loaded: the first sentence states the purpose, the second explains the interactive behavior, and the third gives the conditional usage and return outcome. No wasted words; every sentence adds distinct value.

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?

The tool has no parameters and an output schema (though details aren't shown). The description covers when to call, the interactive nature, and the return behavior. It explains the condition for not calling again. An agent has enough context to decide when and how to use this tool correctly.

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?

There are no parameters, so the schema covers all (100%). The description does not need to explain parameters. It adds value by describing the interaction and return behavior, but since there are no parameters, a baseline of 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb and resource: 'Ask the user whether to turn local-llm-mcp on for this session.' It is specific about the action and target. However, it does not explicitly differentiate from sibling tools by name, although the name and description make the intent obvious. It does not say 'use this instead of X', so it falls short of a 5.

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?

The description explicitly states when to call it: 'Call it once when a step would print far more than you need or would touch private data.' It also gives a when-not: 'If the user declined, do not call it again unless they ask.' This provides clear usage boundaries and is actionable for an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.