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OpenMonetis MCP

OpenMonetis MCP

by Welington98
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Status da IA

get_ai_status
Read-onlyIdempotent

Check whether the AI assistant is enabled and view connected providers, permitted tools, and shared data scopes for an installation.

Instructions

Mostra se o assistente de IA está habilitado na instalação, quais provedores estão conectados, as ferramentas permitidas e os escopos de dados compartilhados.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_ai_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.3

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered structurally. The description adds little beyond that: it says what information is exposed but not how it is scoped (per-installation vs per-user) or how fresh it is.

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?

A single sentence, front-loaded with the core question (is the AI assistant enabled) followed by the supporting details. Nothing is redundant or padded.

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?

For a zero-parameter read-only status tool with no output schema, the description usefully enumerates the returned fields (enabled flag, providers, tools, data scopes), compensating for the absent output schema. It stops short of describing value formats, but an agent has enough to call it and interpret the result.

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 the description has no parameter meaning to add; baseline 4 applies. The schema is trivially complete for an empty object.

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 names a specific verb ('Mostra') and a precise resource ('se o assistente de IA está habilitado na instalação'), then enumerates the four facts returned: providers connected, allowed tools, shared data scopes. That enumeration distinguishes it from siblings like get_ai_chat and get_ai_diagnosis, though no sibling is named explicitly.

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?

There is no explicit when-to-use statement, no exclusions, and no alternatives named. The use case is only implied by the returned contents – check AI availability and provider connections before relying on AI features – which an agent must infer.

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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