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check_connections

Returns configuration and connection status for local generative AI services, so you can verify setup and troubleshoot connectivity before use.

Instructions

ローカル生成AIの設定・接続状況を返す(現在は設定エラー)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose a meaningful behavioral trait — that the connection currently resolves to a configuration error — which an agent should know before calling. However, it says nothing about whether the check is read-only, whether it triggers reconnection attempts, or what triggers the error.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler; the core purpose comes first and the caveat follows. It is efficient, though the trailing parenthetical reads as an operational note rather than tool documentation.

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?

An output schema exists, so return values need not be described, and there are no parameters to document. Still, for a diagnostic tool with no annotations, the description omits whether it is a safe read-only probe and whether the 'configuration error' note is transient or persistent, leaving the agent to guess.

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 tool takes zero parameters, so there is no parameter semantics to explain; the baseline for a no-arg tool is 4. Nothing in the description is required to compensate for a schema gap.

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?

States a specific verb and resource: returns the configuration/connection status of the local generative AI. It even flags the current runtime state (a configuration error), which tells the agent what to expect. No siblings exist to differentiate from, so the ceiling is 4 rather than 5.

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

Usage Guidelines2/5

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

There is no explicit when-to-use guidance, no preconditions, and no alternatives (none exist). The intent is only weakly implied by the name and the status-reporting phrasing.

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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