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antigravity-desktop-live-quota-mcp

antigravity-desktop-live-quota-mcp

by Mefamex

get_quota_status

Queries live AI model quota and usage status, including Gemini 5-hour/weekly, Claude/GPT, and Argon, through Antigravity's local language server to check limits and resets.

Instructions

Antigravity yerel language_server üzerinden canlı AI model kota ve kullanım durumunu (Gemini 5h/weekly, Claude/GPT, Argon) sorgular.

Input Schema

NameRequiredDescriptionDefault
include_rawNoTrue ise sunucudan gelen saf ham yanıtı da ekler.
force_refreshNoTrue ise yerel dil sunucusu önbelleğini temizleyip doğrudan buluttan anlık çeker.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It discloses that results come from the local language_server and are 'canlı' (live), but it says nothing about what happens when the server is not running, whether the call is read-only, latency/rate-limit expectations, or the shape of the returned quota data (for example 5-hour vs weekly windows). It also omits the cache-clearing side effect that force_refresh causes, which is left entirely to the schema.

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 that identifies the source (local language_server) and the queried data without filler. It is appropriately sized for a two-optional-parameter read tool, losing a point only because the parenthetical provider list is somewhat compressed and unexplained.

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?

With no output schema and no annotations, the description should compensate more than it does: it never says what the response contains beyond listing providers, nor how quota windows are represented. For a simple read-only status query it is adequate, but an agent still cannot predict failure modes or return structure.

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?

Schema description coverage is 100%, so both include_raw and force_refresh are already documented in the input schema, which sets the baseline at 3. The description adds only that data is live, and it does not explain how include_raw or force_refresh alter the returned quota figures beyond what the schema states.

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 specific verb (sorgular = queries) and a specific resource (live AI model quota and usage status), and it enumerates the covered providers (Gemini 5h/weekly, Claude/GPT, Argon). That is enough for an agent to distinguish it from status-oriented siblings like get_instance_status or reinject_badge, though it never names those siblings 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?

The purpose implies when the tool is relevant (checking remaining model quota/usage), but there is no explicit when-to-use/when-not-to-use statement and no routing to the sibling tools. An agent can infer the trigger from the name and description alone, which is the definition of minimum-viable guidance.

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